THE RAPID progress and technological advances in batteries
|
|
- Rosalind Moore
- 5 years ago
- Views:
Transcription
1 1892 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 22, NO. 5, SEPTEMBER 2014 Active Diagnosability of Discrete Event Systems and its Application to Battery Fault Diagnosis Ziqiang Chen, Feng Lin, Caisheng Wang, Le Yi Wang, and Min Xu Abstract A battery system may consist of many batteries; each battery can have a normal operating mode and several faulty modes. This makes the fault status of a battery system very complex. To diagnose such a complex system, passive diagnosis is often insufficient. We may need to actively control the system to complete the diagnosis task. In this brief, we investigate the active diagnosis in the framework of discrete event systems. We model the system to be diagnosed by an automaton (finite state machine) with state outputs in which some events are controllable in the sense that they can be enforced, and some events are not. We say that the system is actively diagnosable if we can find a control under which the faults can be diagnosed. We derive a necessary and sufficient condition for a system to be actively diagnosable. Algorithms are devised for checking active diagnosability and finding controls that achieve it. The theoretical results are then applied to fault diagnosis of battery systems. We illustrate the approach using a simplified battery system consisting of four batteries. We find a control that diagnoses the faults based on the measurements of two temperature sensors. Index Terms Battery management system, detectability, diagnosability, discrete event systems (DESs), fault diagnosis. I. INTRODUCTION THE RAPID progress and technological advances in batteries have led to a wide usage of battery systems for electric vehicles and other applications. As battery systems become increasingly complex, issues of safety and reliability of operation are of great importance. Many researchers from the control and artificial-intelligence communities have devoted to fault detection study and diagnosis for battery systems in [3] and [6], taking both qualitative and quantitative approaches. Different strategies are adopted to achieve the diagnosis goals. For example, modified floating search algorithms for repeated feature selection are presented in [11] for fault detection. Signal processing techniques, such as particle-filtering, have been applied to fault detection of batteries in [1]. In addition, considerable efforts have been devoted to bridging the Manuscript received June 13, 2013; revised September 10, 2013; accepted November 9, Manuscript received in final form November 12, Date of publication November 28, 2013; date of current version July 24, This work was supported in part by the National Science Foundation of USA under Grant ECS and Grant ECS , and in part by the National Natural Science Foundation of China under Grant , Grant , Grant , and Grant Recommended by Associate Editor F. Basile. Z. Chen and M. Xu are with the School of Mechanics and Power Engineering, Shanghai Jiao Tong University, Shanghai , China ( chenziqiang@sjtu.edu.cn; mxu@sjtu.edu.cn). F. Lin is with the Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI USA, and also with the School of Electronics and Information Engineering, Tongji University, Shanghai , China ( flin@ece.eng.wayne.edu). C. Wang and L. Y. Wang are with the Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI USA ( caisheng@eng.wayne.edu; lywang@eng.wayne.edu). Digital Object Identifier /TCST methodologies of the control and artificial-intelligence communities [7]. Failure diagnosis addresses the problem of identifying and isolating deviations of the actual behavior of a dynamic system from its desired behavior. However, a large-scale battery system consists of many batteries, which have different characteristics even when they are new. Moreover, their characteristics and dynamics change with time and operating conditions due to aging, environments, and chemical property variations. Therefore, more advanced approaches must be used to meet the demands of diagnosis of battery systems with dynamic variation of characteristics. In recent years, various approaches that are based on a discrete event systems (DESs) modeling formalism have been proposed. DESs are characterized by asynchronous occurrences of discrete events. The behavior of DESs can be observed as possible transitions between different states following the occurrence of events. DESs are interesting as they are omnipresent around us: computer and communication systems, automated manufacturing systems, traffic systems, intelligent transportation systems, database systems, software systems, and so on. The DES framework has been used to investigate many important issues such as controllability and observability [5]. DESs are often used to model complex systems and faults are more likely to occur in complex systems. Therefore, we need more advanced diagnostic tools for DES. It is essential for fault diagnosis that each fault can be uniquely identified based on partial observations of the system behavior. Diagnosis of DES has been investigated extensively. In [13], a fault is modeled as an event. It is assumed that some events are observable and some other events are not observable. Naturally, faulty events are assumed to be unobservable. A DES is said to be diagnosable if the occurrences of faulty events can be determined after some finite observations of observable events. Since then, much work has been done for diagnosis and diagnosability of DES [14], [18], including the approaches using Petri nets [2], [4]. We start our investigation of diagnosability of DES in [8], where faults are described by states (rather than events, as in [13] and subsequent publications). To diagnose a DES is to determine, which state or set of states the system is currently in. The diagnosis is based on state outputs rather than event observations. Both off-line and on-line diagnoses are discussed in [8]. We also investigate issues related to diagnosis, namely, detectability [15] [17] and opacity [9] in the DES framework. In this brief, we investigate active diagnosis for DESs. We assume that faults are described by states as in [8], that is, we specify a faulty status of a system by partitioning the state set into different cells. One cell represents the normal mode IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.
2 CHEN et al.: ACTIVE DIAGNOSABILITY OF DESs 1893 of the system. The other cells represent the faulty modes. The goal of diagnosis is to determine, which cell the system is in. The diagnosis is based on the state output, which is a mapping from the state set to an output set. The output mapping is many to one, so the current state of the system cannot be determined from the current output observations alone. Therefore, we need to control the system along certain trajectories so that the fault can be diagnosed. The control is achieved by enforcing some controllable events (not all events are controllable). We say a system is actively diagnosable if there exists a control such that after the execution of the control, the faulty status of the system, as represented by the partition, can be determined. We derive a necessary and sufficient condition for a DES to be actively diagnosable. The condition is based on the state estimates after a sequence of control actions and observations. Given a DES and a specification of faulty status, the condition can be checked by manipulation on the automata. We develop an algorithm that can be used to check the necessary and sufficient condition for active diagnosability and to find a control that achieves it if the necessary and sufficient condition is satisfied. Note that while checking whether a system is actively diagnosable is done off-line, the actual diagnosis is performed on-line. The theoretical results are applied to battery system diagnosis. We consider a simplified but representative battery system consisting of four batteries. They are combined in both parallel and series connections. Two types of faults are considered: aged cell and increased internal resistance. These two faults are similar and difficult to diagnose. Two temperature sensors are installed that provide the outputs. We construct a DES model for this battery system and show that the system is actively diagnosable. In addition, we construct a control strategy that diagnoses the battery system using the algorithm we proposed. The diagnosis is an important problem in battery systems. Our approach provides a systematic way to solve this difficult but important problem. Active diagnosis using control has been investigated in DESs. The first work on active diagnosis appears in [14], where faults are modeled as events. However, to the best of our knowledge, the active diagnosability of DESs as defined in this brief has never been proposed before. II. ACTIVE DIAGNOSABILITY OF DESS We model a DES to be diagnosed as G = (Q,,δ,Y, h) where Q is the set of states, is the set of events, Y is the output space, δ : Q Q is the state transition function, q = δ(q,σ) is the next state if event σ occurs at state q, h : Q Y is the output function, y = h(q) is the observed output when the system is at state q. δ : Q Q is extended to δ : Q Q in the usual way. We assume that all states in G are legal and accessible. If there are illegal states in the system, we assume that a supervisor has already been designed to control the system to stay within the legal states and G represents the controlled system. States of the system describe its conditions. To diagnose a fault is to identify, which state or set of states the system G is in. Depending on the requirements on diagnostics, we partition the state space Q into disjoint subsets (cells) and denote the resulting partition by T. The states in the same cell are viewed as equivalent as far as faults under consideration are concerned. We use q = T q to denote that q and q are in the same cell. Our model is rather general since we do not impose any restrictions on T. We use active control to diagnose the system. Therefore, we designate a set of controllable events c, where the controllability of events is interpreted in a strong sense: a controllable event can be made to occur if it is allowed in the system G. Not all events are controllable for diagnosis. Only events in c can be controlled (or enforced). We denote by c the set of all strings of events over c including the empty string ε. Astringu c is called a control. The goal of diagnostics is to find which cell of T the system G belongs to by issuing a control u c and observing the output. To know what output is expected from G, we need to know what is the behavior of G. The behavior is described by all possible strings of events that can occur in G. Note that after control u is issued, the behavior of G is restricted, because G must execute the events in u and execute them in the order given by u. Simultaneously, some uncontrollable events in \ c may also occur. Therefore, the behavior of G under control u is described by B(u) = P 1 (u) where P : c is the inverse projection of the natural projection P : c defined in the usual way. The set of possible states Q Q that the system G may be in currently is called the (current) state estimate. Suppose that the current state estimate is Q i, and the current output (observation) is y i. Let us find the state estimate after the execution of a controllable event σ i+1 or the observation of a new output y i+1, or both. To unify the notation, we use (σ i+1, y i+1 ) to denote a new control execution, or a new output observation, or both as follows. If a new output is observed without new control execution, then σ i+1 = ε (the empty string), that is, (σ i+1, y i+1 ) = (ε, y i+1 ). If a new control is executed but no change in the output, then y i+1 = y i,thatis, (σ i+1, y i+1 ) = (σ i+1, y i ). If a new control is executed and a new output is observed, then σ i+1 = ε and y i+1 = y i.using this notation, a trajectory of G can be described by a sequence w = (σ 1, y 1 )(σ 2, y 2 )...(σ n, y n ). We can calculate the state estimates along a trajectory recursively as follows. The initial state estimate Q 0 is given, depending on our knowledge of initial state. In the worst case, when no knowledge on the initial state is available, we let Q 0 = Q. The initial output y 0 is also known. Clearly, Q 0 h 1 (y 0 ), that is, the initial state estimate must be consistent with the initial output (observation), otherwise, the initial state estimate can be updated. The state estimate immediately after (σ 1, y 1 ) is given by NOR((Q 0, y 0 ), (σ 1, y 1 )) ={q Q : ( q Q 0 )q = δ(q,σ 1 ) h(q) = y 1 }.
3 1894 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 22, NO. 5, SEPTEMBER 2014 NOR((Q 0, y 0 ), (σ 1, y 1 )) is called the next observation reach after (σ 1, y 1 ). Note that from Q 1 = NOR((Q 0, y 0 ), (σ 1, y 1 )), the system can move to other states without being controlled. We can calculate the state estimate before the next transition (σ 2, y 2 ) as follows: SOR(Q 1, y 1) ={q Q : ( q Q 1 )( s ( c) )q = δ(q, s) ( t s)h(δ(q, t)) = y 1 } where t s means that t is a prefix of s. SOR(Q 1, y 1) is called the same observation reach. Q 1 = SOR(Q 1, y 1) is the state estimate before the next transition (σ 2, y 2 ). To find state estimates after all possible trajectories of the system, we define a new automaton as follows: G =(X,,ξ, x o ) = Ac(2 Q Y,( c {ε}) Y,ξ,(Q 0, y 0 )) where Ac(.) denotes the accessible part. The event set of G is = ( c {ε}) Y, the set of all possible (σ i, y i ).The state of G is X = 2 Q Y, the set of possible states estimates and observations. The initial state of G is x o = (Q 0, y 0 ),the initial state estimate and initial observation. The state transition function ξ : X X is defined as follows. For x i = (Q i, y i ) and σ i+1 = (σ i+1, y i+1 ) ξ(x i, σ i+1 )=(SOR(NOR((Q i,y i ), (σ i+1,y i+1 )), y i+1 ),y i+1 ). ξ : X X is extended to ξ : X X in the usual way. The automaton G tells us state estimate after any trajectory s shown in the following theorem. Theorem 1 : Let the initial state estimate and initial observation be x o = (Q 0, y 0 ). Let the trajectory of the system be w = (σ 1, y 1 )(σ 2, y 2 )...(σ n, y n ). Denote x n = (Q n, y n ) = ξ(x o, w). Then, the state estimate after w is given by Q n. Proof: We prove the result by induction on the length of w, denoted by w, as follows. Base: Since the initial state is x o = (Q 0, y 0 ), the result is obviously true for w =0 (that is, w = ε). Induction Hypothesis: Assume that the result is true for w k. Induction Step: We show that the result is true for w = k +1. Denote w = w (σ k+1, y k+1 ), x k = (Q k, y k ) = ξ(x o, w ), x k+1 = (Q k+1, y k+1 ) = ξ(x o, w) = ξ(x k,(σ k+1, y k+1 )). By induction hypothesis, Q k is the state estimate after w.by the definition of the next observation reach, the state estimate immediately after σ i+1 = (σ i+1, y i+1 ) is given by NOR((Q k, y k ), (σ k+1, y k+1 )). By the definition of same observation reach, the state estimate before the next transition is given by SOR(NOR((Q k, y k ), (σ k+1, y k+1 )), y k+1 ). On the other hand, since ξ(x i, σ i+1 ) = (SOR(NOR((Q i, y i ), (σ i+1, y i+1 )), y i+1 ), y i+1 ) = (Q k+1, y k+1 ) SOR(NOR((Q i, y i ), (σ i+1, y i+1 )), y i+1 ) = Q k+1. That is, Q k+1 is the state estimate after w. This proves the result. Automaton G gives the state estimate after the control and observation described by w. We denote the state estimate after w by ψ(w), thatis,forw = (σ 1, y 1 )(σ 2, y 2 )...(σ n, y n ) and x n = (Q n, y n ) = ξ(x o, w), the state estimate is denoted by ψ(w) = Q n. For some w, we can determine system s fault status as described by T after w, while for some other w, we cannot. In the first case, we say w is diagnosable and in the second case, we say w is not diagnosable. Formally, we say that w is diagnosable if ( q, q ψ(w))q = T q. Define the set of marked states in G = (X,,ξ, x o ) as follows: X m ={x = (Q, y) X : ( q, q Q)q = T q }. In other words, X m is the set of states at which system s fault status can be determined. The following proposition gives a necessary and sufficient condition for wto be diagnosable. Proposition 1: For any w, w is diagnosable if and only if w leads from the initial state x o to a marked state in X m,that is, x = ξ(x o, w) X m. Proof: It follows from the definition of X m. Clearly, the above discussions are still valid if we start not at the initial state x o but some current state x c. We will consider the current state x c from now on. To this end, denote the language generated by G from state x c by L( G, x c ). The goal of active diagnosis is to use a control u from the current state x c so that the resulting w is diagnosable. The problem is not simple because w is not unique for a given control u. To solve the problem, let us find the relation between w and u. Forw = (σ 1, y 1 )(σ 2, y 2 )...(σ n, y n ), let us define θ(w) = σ 1 σ 2...σ n. Note that some σ i may be ε. Inotherwords θ : c. Obviously, the control corresponding to w is given by u = θ(w). Denote the inverse mapping of θ by θ 1. Then clearly, for a control u c from the current state x c,thesetofall possible w is given by θ 1 (u) L( G, x c ). We say that a control u diagnoses the system G if all w in θ 1 (u) L( G, x c ) are diagnosable, that is ( w θ 1 (u) L( G, x c )) ξ(x c, w) X m. We say that a control u c is feasible if all events in u are defined in G in all circumstances, that is ( u u)θ 1 (u ) L( G, x c ) θ 1 (u) L( G, x c ) where θ 1 (u) L( G, x c ) denotes the prefix closure of θ 1 (u) L( G, x c ). The above expression says that any string in θ 1 (u ) L( G, x c ) can be continued to a string in θ 1 (u) L( G, x c ). In other words, there are no blocking in executing u. To ensure that a control works, we must make sure that it is feasible. In summary, we define active diagnosability as follows. Definition 1: A system G is actively diagnosable with respect to T if from any state, there exists a feasible control u c that diagnoses G, thatis ( x c X)( u c )( w θ 1 (u) L( G, x c ))ξ(x, w) X m ( u u)θ 1 (u ) L( G, x c ) θ 1 (u) L( G, x c ).
4 CHEN et al.: ACTIVE DIAGNOSABILITY OF DESs 1895 Given a system G and a partition T representing the fault status, we want to determine if G is actively diagnosable with respect to T. If it is, then we want to find a control u that diagnoses G. To do this, we start with the automaton G = (X,,ξ, x o ). We replace the labels for all transitions as follows: every σ = (σ, y) is replaced by σ. The resulting new automaton is a nondeterministic automaton with ε-transitions (note that some σ are ε). We can convert this nondeterministic automaton with ε-transitions into a deterministic automaton in the usual way [5] and denote the deterministic automaton by Algorithm 1 (Active Diagnosis) G obs = (Z, c,υ,z o ) = Ac(2 X, c,υ,z o ). Note that a state z Z in G obs is a subset of states in G, that is, z ={x 1, x 2,...} X. Define the marked states in G obs as Z m ={z Z : z X m }. In other words, a state z = {x 1, x 2,...} is marked if all its elements are marked in G. When G obs is in a marked state, the system s fault status can be determined. If from any state z, there exists a string u that reaches a marked state in Z m, then that string u has potential to be used as a control. One thing that needs to be ensured is that u is feasible. To ensure feasibility, let us distinguish these transitions in G obs that can never be blocked from those transitions that may be blocked as follows. Denote the set of all transitions as υ ={(z,σ,υ(z,σ)): υ(z,σ) is defined} (as common in DES, υ is used to denote both the transition function and the set of transitions). Define the set of marked transitions as υ m ={(z,σ,z ) : ( (x i = (Q i, y i ) z)( q Q i ) ( t ( c ) )δ(q, tσ) is defined}. In other words, a transition (z,σ,z ) υ m is marked if and only if σ is feasible from any relevant state. Hence, a string from z whose transitions are all marked is a feasible control. In summary, the following theorem gives a necessary and sufficient condition for a system to be actively diagnosable. Theorem 2: AsystemG is actively diagnosable with respect to T if and only if in the observer G obs, all states are coaccessible to a marked state via some marked transitions, that is ( z Z)( u c )υ m(z, u) Z m. Proof: By the property of the observer G obs υ(z, u) ={x X : ( x c z)( w θ 1 (u))x = ξ(x c, w)}. Therefore ( z Z)( u c )υ(z, u) Z m ( z Z)( u c )υ(z, u) X m ( z Z)( u c ){x X : ( x c z)( w θ 1 (u))x = ξ(x c, w)} X m ( z Z)( u c )( x X)( x c z)( w θ 1 (u))x = ξ(x c, w) x X m ( x c X)( u c )( w θ 1 (u) L( G, x c ))ξ(x, w) X m Furthermore, from the definition of marked transitions, we know that u is feasible if and only if all its transitions are marked. Based on the above results, we can check whether a system is actively diagnosable and if it is, find a control that diagnoses the faults as summarized in Algorithm 1. The computational complexity of Algorithm 1 is rather high. Since the number of states in G is up-bounded by X =2 Q Y, the number of states in G obs is up-bounded by 2 X = 2 2 Q Y. In other words, the computational complexity is double exponential. Let us now apply the theoretical results to fault diagnosis in battery management systems. III. FAULT DIAGNOSIS IN BATTERY SYSTEMS In this section, we consider the diagnosis problem for networked battery systems. There are many possible topologies to construct such a networked battery system, each with different system costs and diagnosability. The goal here is to illustrate diagnosability analysis for some typical network topologies so that the method can be applied to large scale systems. Different measurements (sensors) can be used for diagnosis. For example, we can use voltmeters and ammeters to measure voltages and currents. If a battery s terminal current and voltage can be measured and its load can be managed to provide sufficiently rich excitation, then its internal parameters, such as internal resistance, maximum capacity, state of charge, and polarization coefficients, can be estimated and used to monitor and diagnose the battery [10]. These methods are very useful at the continuous variable level. This brief focuses on active diagnosis at the discrete event level, which is more abstract and more suitable for large scale battery systems. For such a large battery system, switches are used in the system to control charging and discharging of different parts of the system. For example, in electric vehicles or
5 1896 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 22, NO. 5, SEPTEMBER 2014 Fig. 1. Structure of a battery management system for diagnosis. hybrid vehicles, the battery pack can be reconfigured through electronic switches, wherein a selectable number of battery modules may be connected either in a serial configuration or in a parallel configuration [21], and each pack can be flexibly controlled for charging or discharging [22]. For diagnosis, the commands of charging and discharging of batteries are issued by a battery management system. The signals and data measured by sensors are collected and processed by the data acquisition module; then these commands and data are fed into the diagnosis module for diagnostics. The structure of a battery management system for diagnosis is shown in Fig. 1. To illustrate the battery management system for diagnosis, let us consider a battery system that consists of four batteries. We list in Fig. 2 some of the possible topologies and measurement/switch configurations. We note that the list is far from being exhaustive and other configurations are possible. The main question is how to check diagnosability for a given topologies and measurement/switch configuration. We note that for simple topologies and configurations, we may intuitively evaluate diagnosability. However, to evaluate many possible network topologies and measurement/switch configurations, the systematic approach of this brief becomes a significant advantage in providing an automated and comprehensive tool for battery system design. We also note that the measurement devices and switches introduce costs and reliability issues themselves. Therefore, it is desirable that we use as few such components as possible while maintaining diagnosability. The common faults in batteries are open circuit, short circuit, high internal resistance, aging, capacity loss, high selfdischarge, overheating, and so on. Among them, high internal resistance and aging are more difficult to diagnose and hence are investigated in this brief. They are described as follows. Internal resistance increasing is usually caused by chemical changes in materials, gas generation, poor solid electrolyte interface inside the battery, poor contacts, etc. This fault will cause temperature to increase when the battery is charging or discharging and aging: over-heating, poor environment temperature and operating condition, and so on, can all cause battery aging, resulting in increased temperature during battery charging/discharging but not as much as the one caused by increased internal resistance. Aging will result in loss of Fig. 2. Some potential network topologies and measurement/switch configurations i = 1, 2, 3, 4forBiandTi;i = 1, 2forSWi. battery capacity. This degradation of battery capacity is a major reason for a battery to retire from its normal operation. We use temperature sensors for diagnosis in this brief. In practice, other sensors can be used as well. We assume that thermal dynamics of the battery system has been investigated at the continuous variable level and it has been summarized and abstracted to the discrete event level as to be described shortly. Although both internal resistance increasing and aging will cause temperature to increase in charging and discharging, the temperature increasing due to increased internal resistance is faster than that caused by aging [12]. We can use the active diagnosability theory developed in the previous section for battery diagnosis. We illustrate our solution using the battery system shown in Fig. 2(c). The system has four batteries. Batteries 1 and 2 are connected in serial and must be charged or discharged together, and similarly for batteries 3 and 4. There are two switches; one controls the charging and discharging of batteries 1 and 2, and the other for batteries 3 and 4. There are two temperature sensors in the system, one at the middle of batteries 1 and 3 that can measure the temperature of batteries 1 and/or 3, the other at the middle of batteries 2 and 4. For battery i, i = 1, 2, 3, 4 we define the following events. α i : aging; β i : internal resistance increasing; λ i : start charging; μ i : stop charging; η i : start discharging; and σ i : stop discharging. The events λ i, μ i, η i,andσ i are controllable, because we can issue commands to force these events. While the events α i and β i are uncontrollable, because we cannot prevent these abnormities and faults from occurring. Besides the normal mode, we consider two faulty modes: one for aging and one for internal resistance increasing. Therefore, the DES model for battery i, i = 1, 2, 3, 4, denoted by G i, is shown in Fig. 3.
6 CHEN et al.: ACTIVE DIAGNOSABILITY OF DESs 1897 TABLE I OUTPUT MAPPINGS OF SENSORS 1 AND 2 Fig. 3. DES model for G i Battery i. A state of G i consists of two parts. The first part describes the charging status of the battery: charging, discharging, and idle (neither charging nor discharging). The second part describes the fault status of the battery: normal, aging, and internal resistance increasing. Therefore, the meaning of states is as follows. A i : (charge, aging); B i : (charge, normal); C i : (charge, internal resistance increasing); D i : (idle, aging); N i : (idle, normal); F i : (idle, internal resistance increasing); E i : (discharge, aging); H i : (discharge, normal); and J i : (discharge, internal resistance increasing). The initial state is N i. The states of G i are partitioned into three cells, one for normal, one for aging, and one for internal resistance increasing. In other words, the partition specifying fault is given by T i ={{B i, N i, H i }, {A i, D i, E i }, {C i, F i, J i }}. When the system is in the cell {B i, N i, H i }, its fault status is normal. When the system is in the cell {A i, D i, E i }, its fault status is aging. When the system is in the cell {C i, F i, J i }, its fault status is internal resistance increasing. When the battery is in states B i, D i, N i, F i,andh i, its temperature will not increase. When the battery is in states A i and E i, its temperature will increase slowly. When the battery is in states C i and J i, its temperature will increase fast. The temperature changes will be sensed by the temperature sensor closest to the battery (Sensor 1 is closest to batteries 1 and 3), but not by the other temperature sensor. To define the output mapping, let us denote the set of outputs from Sensor 1 by Y 1 = {1, 2, 3}, where the symbols (numbers) are to be interpreted as follows: 1) no temperature increase; 2) slow temperature increase; and 3) fast temperature increase. Fig. 4. Part of the overall system G = G 1 G 2 G 3 G 4. Similarly, denote the set of outputs from Sensor 2 by Y 2 = {1, 2, 3}. The output mappings of Sensor 1 due to Battery 1, denoted by h 11, and Sensor 2 due to Battery 1, denoted by h 21, are given in the Table I. The other output mappings h ki, k = 1, 2, i = 1, 2, 3, 4, are defined similarly. With G i, T i,andh ki, k = 1, 2, i = 1, 2, 3, 4 defined for each battery, we can combine them to obtain the DES model for the entire battery system as follows. Since batteries 1 and 2 are in serial, they must be charged and discharged at the same time. Therefore, λ 1 = λ 2,μ 2 = μ 2,η 1 = η 2,σ 1 = σ 2. Similarly, λ 3 = λ 4,μ 4 = μ 3,η 3 = η 4,σ 3 = σ 4. The entire battery system with four batteries is modeled by the well-defined parallel composition [5] G = G 1 G 2 G 3 G 4. Since parallel composition is well defined, G can be calculated automatically using available software such as TCT [19] or UMDES [20]. G has 6561 states. Part of G is shown in Fig. 4. The state of G is denoted by q = (q 1, q 2, q 3, q 4 ). For example, q = (C 1, B 2, N 3, N 4 ) means that battery 1 is in state C 1, battery 2 is in state B 2, and so on. The partition specifying fault is the conjunction of all T i T = T 1 T 2 T 3 T 4. In other words, for q = (q 1, q 2, q 3, q 4 ) and q = (q 1, q 2, q 3, q 4 ) q = T q q 1 = T1 q 1 q 2 = T2 q 2 q 3 = T3 q 3 q 4 = T4 q 4.
7 1898 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 22, NO. 5, SEPTEMBER 2014 The output mapping of sensors is given by h = h 1 h 2 = (max{h 11, h 12, h 13, h 14 }) (max{h 21, h 22, h 23, h 24 }). In other words, for q = (q 1, q 2, q 3, q 4 ) h(q) = (max{h 11 (q 1 ), h 12 (q 2 ), h 13 (q 3 ), h 14 (q 4 )}, max{h 21 (q 1 ), h 22 (q 2 ), h 23 (q 3 ), h 24 (q 4 )}). The reason for max is that the temperature reading of a sensor will increase if the temperature of one of the two batteries it measures increases. With G, T,andh given above, we can use Algorithm 1. We find that the battery system is actively diagnosable and one control to diagnose the system is given by u = λ 1 μ 1 η 3 σ 3.It describes the following diagnosis process. Step 1) Start charging batteries 1 and 2 (λ 1 = λ 2 ) and observe the output. There are 3 3 = 9 possible outputs. Some of them are listed below. If h(q) = (1, 1), then both batteries 1 and 2 are normal. If h(q) = (2, 1), then battery 1 is aging and battery 2 is normal. If h(q) = (1, 2), then battery 1 is normal and battery 2 is aging. If h(q) = (2, 2), then both battery 1 and battery 2 are aging. Step 2) Stop charging batteries 1 and 2 (μ 1 = μ 2 ).No output needs to be observed. Step 3) Start discharging batteries 3 and 4 (η 3 = η 4 ) and observe the output. There are 3 3 = 9 possible outputs. Some of them are listed below. If h(q) = (3, 3), then both batteries 3 and 4 s internal resistances are increasing. If h(q) = (3, 2), then battery 3 s internal resistance is increasing and battery 4 is aging. If h(q) = (2, 3), then battery 3 is aging and battery 4 s internal resistance is increasing. Step 4) Stop charging batteries 3 and 4 (σ 3 = σ 4 ). The above is only one possible control. There are other controls that can diagnose the battery system. Our approach provides a systematic way to determine the active diagnosability and find the appropriate control. The method can be automated using Algorithm 1. IV. CONCLUSION This brief investigates active diagnosis of DESs and applies the results to the important problem of fault diagnosis of battery systems. The main contributions of this brief are as follows: 1) a new DES model is proposed for studying active diagnosability of DESs, where diagnosis is achieved by actively controlling the system; 2) a new definition of active diagnosability is introduced, which captures the ability to diagnose a system using control; 3) a necessary and sufficient condition is obtained for a system to be actively diagnosable; 4) an algorithm is devised to check active diagnosability and to find a control if the system is actively diagnosable; and 5) the results are used to study fault diagnosis of complex battery systems. REFERENCES [1] M. Abbas, A. A. Ferri, M. E. Orchard, and G. J. Vachtsevanos, An intelligent diagnostic/prognostic framework for automotive electrical systems, in Proc. IEEE Intell. Veh. Symp., Istanbul, Turkey, Jun. 2007, pp [2] F. Basile, P. Chiacchio, and G. De Tommasi, An efficient approach for online diagnosis of discrete event systems, IEEE Trans. Autom. Control, vol. 54, no. 4, pp , Apr [3] M. Blanke, M. Kinnaert, J. Lunze, and M. Staroswiecki, Diagnosis and Fault-Tolerant Control, 2nd ed. New York, NY, USA: Springer-Verlag, [4] M. P. Cabasino, A. Giua, and C. Seatzu, Fault detection for discrete event systems using Petri nets with unobservable transitions, Automatica, vol. 46, no. 9, pp , Sep [5] C. G. Cassandras and S. Lafortune, Introduction to Discrete Event Systems, 2nd ed. New York, NY, USA: Springer-Verlag, [6] J. Korbicz, J. M. Koscielny, Z. Kowalczuk, and W. Cholewa, Fault Diagnosis. New York, NY, USA: Springer-Verlag, [7] G. Lamperti and M. Zanella, A bridged diagnostic method for the monitoring of polymorphic discrete-event systems, IEEE Trans. Syst., Man, Cyber. B, Cyber., vol. 34, no. 5, pp , Oct [8] F. Lin, Diagnosability of discrete event systems and its applications, Discrete Event Dyn. Syst., Theory Appl., vol. 4, no. 1, pp , [9] F. Lin, Opacity of discrete event systems and its applications, Automatica, vol. 47, no. 3, pp , [10] L. Liu, L. Y. Wang, Z. Chen, C. Wang, F. Lin, and H. Wang, Integrated system identification and state-of-charge estimation of battery systems, IEEE Trans. Energy Convers., vol. 28, no. 1, pp , Mar [11] J. I. Park, S. H. Baek, M. K. Jeong, and S. J. Bae, Dual features functional support vector machines for fault detection of rechargeable batteries, IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 39, no. 4, pp , Jul [12] S. B. Peterson, J. Apta, and J. F. Whitacre, Lithium-ion battery cell degradation resulting from realistic vehicle and vehicle-to-grid utilization, J. Power Sour., vol. 195, no. 8, pp , Apr [13] M. Sampath, R. Sengupta, S. Lafortune, K. Sinnamohideen, and D. Teneketzis, Diagnosability of discrete-event systems, IEEE Trans. Autom. Control, vol. 40, no. 9, pp , Sep [14] M. Sampath, S. Lafortune, and D. Teneketzis, Active diagnosis of discrete-event systems, IEEE Trans. Autom. Control, vol. 43, no. 7, pp , Jul [15] S. Shu, F. Lin, and H. Ying, Detectability of discrete event systems, IEEE Trans. Autom. Control, vol. 52, no. 12, pp , Dec [16] S. Shu and F. Lin, I-detectability of discrete-event systems, IEEE Trans. Autom. Sci. Eng., vol. 10, no. 1, pp , Jan [17] S. Shu and F. Lin, Delayed detectability of discrete event systems, IEEE Trans. Autom. Control, vol. 58, no. 4, pp , Apr [18] S. Tripakis, Fault diagnosis for timed automata, in Formal Techniques in Real Time and Fault Tolerant Systems (LNCS), vol New York, NY, USA: Springer-Verlag, 2002, pp [19] W. M. Wonham. (2013). TCT Software [Online]. Available: [20] S. Lafortune. (2013). UMDES Software [Online]. Available: [21] Modular electronically reconfigurable battery system, U.S. Patent B2, [22] Device and methods for management of power sources for electric vehicle, CN Patent A, 2012.
Semi-asynchronous Fault Diagnosis of Discrete Event Systems
1 Semi-asynchronous Fault Diagnosis of Discrete Event Systems Alejandro White, Student Member, IEEE, Ali Karimoddini, Senior Member, IEEE Abstract This paper proposes a diagnostics tool for a Discrete-
More informationSemi-asynchronous. Fault Diagnosis of Discrete Event Systems ALEJANDRO WHITE DR. ALI KARIMODDINI OCTOBER
Semi-asynchronous Fault Diagnosis of Discrete Event Systems ALEJANDRO WHITE DR. ALI KARIMODDINI OCTOBER 2017 NC A&T State University http://www.ncat.edu/ Alejandro White Semi-asynchronous http://techlav.ncat.edu/
More informationIN THIS paper we investigate the diagnosability of stochastic
476 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL 50, NO 4, APRIL 2005 Diagnosability of Stochastic Discrete-Event Systems David Thorsley and Demosthenis Teneketzis, Fellow, IEEE Abstract We investigate
More information748 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 54, NO. 4, APRIL 2009
748 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL 54, NO 4, APRIL 2009 An Efficient Approach for Online Diagnosis of Discrete Event Systems Francesco Basile, Member, IEEE, Pasquale Chiacchio, Gianmaria De
More informationIEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART B: CYBERNETICS, VOL. 40, NO. 3, JUNE /$ IEEE
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART B: CYBERNETICS, VOL. 40, NO. 3, JUNE 2010 951 Correspondence State-Feedback Control of Fuzzy Discrete-Event Systems Feng Lin and Hao Ying Abstract
More informationSupervisory control under partial observation is an important problem
2576 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 62, NO. 5, MAY 2017 Technical Notes and Correspondence Supervisor Synthesis for Mealy Automata With Output Functions: A Model Transformation Approach Xiang
More informationAchieving Fault-tolerance and Safety of Discrete-event Systems through Learning
2016 American Control Conference (ACC) Boston Marriott Copley Place July 6-8, 2016. Boston, MA, USA Achieving Fault-tolerance and Safety of Discrete-event Systems through Learning Jin Dai, Ali Karimoddini,
More informationResolution of Initial-State in Security Applications of DES
Resolution of Initial-State in Security Applications of DES Christoforos N. Hadjicostis Abstract A non-deterministic labeled finite automaton is initial-state opaque if the membership of its true initial
More informationMOST OF the published research on control of discreteevent
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 43, NO. 1, JANUARY 1998 3 Discrete-Event Control of Nondeterministic Systems Michael Heymann and Feng Lin, Member, IEEE Abstract Nondeterminism in discrete-event
More informationFORMULAS FOR CALCULATING SUPREMAL CONTROLLABLE AND NORMAL SUBLANGUAGES 1 R. D. Brandt 2,V.Garg 3,R.Kumar 3,F.Lin 2,S.I.Marcus 3, and W. M.
FORMULAS FOR CALCULATING SUPREMAL CONTROLLABLE AND NORMAL SUBLANGUAGES 1 R. D. Brandt 2,V.Garg 3,R.Kumar 3,F.Lin 2,S.I.Marcus 3, and W. M. Wonham 4 2 Department of ECE, Wayne State University, Detroit,
More informationA Polynomial Algorithm for Testing Diagnosability of Discrete Event Systems
A Polynomial Algorithm for Testing Diagnosability of Discrete Event Systems Shengbing Jiang, Zhongdong Huang, Vigyan Chandra, and Ratnesh Kumar Department of Electrical Engineering University of Kentucky
More informationModelling of Railway Network Using Petri Nets
Modelling of Railway Network Using Petri Nets MANDIRA BANIK 1, RANJAN DASGUPTA 2 1 Dept. of Computer Sc. & Engg., National Institute of Technical Teachers' Training & Research, Kolkata, West Bengal, India
More informationIntersection Based Decentralized Diagnosis: Implementation and Verification
Intersection Based Decentralized Diagnosis: Implementation and Verification Maria Panteli and Christoforos N. Hadjicostis Abstract We consider decentralized diagnosis in discrete event systems that are
More informationDecentralized Diagnosis of Discrete Event Systems using Unconditional and Conditional Decisions
Decentralized Diagnosis of Discrete Event Systems using Unconditional and Conditional Decisions Yin Wang, Tae-Sic Yoo, and Stéphane Lafortune Abstract The past decade has witnessed the development of a
More informationDiagnosis of Dense-Time Systems using Digital-Clocks
Diagnosis of Dense-Time Systems using Digital-Clocks Shengbing Jiang GM R&D and Planning Mail Code 480-106-390 Warren, MI 48090-9055 Email: shengbing.jiang@gm.com Ratnesh Kumar Dept. of Elec. & Comp. Eng.
More informationOn the Design of Adaptive Supervisors for Discrete Event Systems
On the Design of Adaptive Supervisors for Discrete Event Systems Vigyan CHANDRA Department of Technology, Eastern Kentucky University Richmond, KY 40475, USA and Siddhartha BHATTACHARYYA Division of Computer
More informationCONTROL SYSTEMS, ROBOTICS AND AUTOMATION Vol. XVI - Qualitative Methods for Fault Diagnosis - Jan Lunze QUALITATIVE METHODS FOR FAULT DIAGNOSIS
QUALITATIVE METHODS FOR FAULT DIAGNOSIS Jan Lunze Ruhr University Bochum,, Germany Keywords: Assumption-Based Truth Maintenance System, Consistency-based Diagnosis, Discrete Event System, General Diagnostic
More informationFailure Diagnosis of Discrete Event Systems With Linear-Time Temporal Logic Specifications
Failure Diagnosis of Discrete Event Systems With Linear-Time Temporal Logic Specifications Shengbing Jiang and Ratnesh Kumar Abstract The paper studies failure diagnosis of discrete event systems with
More informationA Learning-based Active Fault-tolerant Control Framework of Discrete-event Systems
A Learning-based Active Fault-tolerant Control Framework of Discrete-event Systems Jin Dai, Ali Karimoddini and Hai Lin Abstract A fault-tolerant controller is a controller that drives the plant to satisfy
More informationDECENTRALIZED DIAGNOSIS OF EVENT-DRIVEN SYSTEMS FOR SAFELY REACTING TO FAILURES. Wenbin Qiu and Ratnesh Kumar
DECENTRALIZED DIAGNOSIS OF EVENT-DRIVEN SYSTEMS FOR SAFELY REACTING TO FAILURES Wenbin Qiu and Ratnesh Kumar Department of Electrical and Computer Engineering Iowa State University Ames, IA 50011, U.S.A.
More informationTHE simulation of a continuous or discrete time system
770 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART B: CYBERNETICS, VOL. 28, NO. 6, DECEMBER 1998 Discrete Event Representation of Qualitative Models Using Petri Nets Alessandra Fanni, Member,
More informationSymbolic Decentralized Supervisory Control
Symbolic Decentralized Supervisory Control SYMBOLIC DECENTRALIZED SUPERVISORY CONTROL BY URVASHI AGARWAL, B.Eng. a thesis submitted to the department of computing & software and the school of graduate
More informationBridging the Gap between Reactive Synthesis and Supervisory Control
Bridging the Gap between Reactive Synthesis and Supervisory Control Stavros Tripakis University of California, Berkeley Joint work with Ruediger Ehlers (Berkeley, Cornell), Stéphane Lafortune (Michigan)
More informationFailure Diagnosis of Discrete-Time Stochastic Systems subject to Temporal Logic Correctness Requirements
Failure Diagnosis of Discrete-Time Stochastic Systems subject to Temporal Logic Correctness Requirements Jun Chen, Student Member, IEEE and Ratnesh Kumar, Fellow, IEEE Dept. of Elec. & Comp. Eng., Iowa
More informationOptimal Non-blocking Decentralized Supervisory Control Using G-Control Consistency
Optimal Non-blocking Decentralized Supervisory Control Using G-Control Consistency Vahid Saeidi a, Ali A. Afzalian *b, Davood Gharavian c * Phone +982173932626, Fax +982177310425 a,b,c Department of Electrical
More informationDecentralized Modular Control of Concurrent Fuzzy Discrete Event Systems
2010 American Control Conference Marriott Waterfront, Baltimore, MD, USA June 30-July 02, 2010 ThB07.2 Decentralized Modular Control of Concurrent Fuzzy Discrete Event Systems Awantha Jayasiri, George
More informationOn Supervisory Control of Concurrent Discrete-Event Systems
On Supervisory Control of Concurrent Discrete-Event Systems Yosef Willner Michael Heymann March 27, 2002 Abstract When a discrete-event system P consists of several subsystems P 1,..., P n that operate
More informationStéphane Lafortune. August 2006
UNIVERSITY OF MICHIGAN DEPARTMENT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE LECTURE NOTES FOR EECS 661 CHAPTER 1: INTRODUCTION TO DISCRETE EVENT SYSTEMS Stéphane Lafortune August 2006 References for
More informationK-diagnosability of labeled Petri nets
Manuscrit auteur, publié dans "9ème édition de la conférence MAnifestation des JEunes Chercheurs en Sciences et Technologies de l'information et de la Communication - MajecSTIC () ()" MajecSTIC Lille,
More informationDiagnosability Analysis of Discrete Event Systems with Autonomous Components
Diagnosability Analysis of Discrete Event Systems with Autonomous Components Lina Ye, Philippe Dague To cite this version: Lina Ye, Philippe Dague. Diagnosability Analysis of Discrete Event Systems with
More informationTHE power transfer capability is one of the most fundamental
4172 IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 27, NO. 9, SEPTEMBER 2012 Letters Power Characterization of Isolated Bidirectional Dual-Active-Bridge DC DC Converter With Dual-Phase-Shift Control Biao
More informationReducing the Supervisory Control of Discrete- Event Systems under Partial Observation
MODARES JOURNAL OF ELECTRICAL ENGINEERING, VOL 16, NO 4, WINTER 2016 29 Reducing the Supervisory Control of Discrete- Event Systems under Partial Observation Vahid Saeidi, Ali A. Afzalian, and Davood Gharavian
More informationMonitoring and Active Diagnosis for Discrete-Event Systems
Monitoring and Active Diagnosis for Discrete-Event Systems Elodie Chanthery, Yannick Pencolé LAAS-CNRS, University of Toulouse, Toulouse, France (e-mail: [elodie.chanthery, yannick.pencole]@laas.fr) University
More informationAnalysis and Optimization of Discrete Event Systems using Petri Nets
Volume 113 No. 11 2017, 1 10 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu ijpam.eu Analysis and Optimization of Discrete Event Systems using Petri Nets
More informationPetri Net Diagnoser for DES Modeled by Finite State Automata
51st IEEE Conference on Decision and Control December 10-13, 2012. Maui, Hawaii, USA Petri Net Diagnoser for DES Modeled by Finite State Automata Marcos V. Moreira and Felipe G. Cabral and Oumar Diene
More informationNo.5 Node Grouping in System-Level Fault Diagnosis 475 identified under above strategy is precise in the sense that all nodes in F are truly faulty an
Vol.16 No.5 J. Comput. Sci. & Technol. Sept. 2001 Node Grouping in System-Level Fault Diagnosis ZHANG Dafang (± ) 1, XIE Gaogang (ΞΛ ) 1 and MIN Yinghua ( ΠΦ) 2 1 Department of Computer Science, Hunan
More informationDecentralized Failure Diagnosis of Discrete Event Systems
IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS PART A: SYSTEMS AND HUMANS, VOL., NO., 2005 1 Decentralized Failure Diagnosis of Discrete Event Systems Wenbin Qiu, Student Member, IEEE, and Ratnesh Kumar,
More informationIN THIS PAPER, we consider a class of continuous-time recurrent
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II: EXPRESS BRIEFS, VOL. 51, NO. 4, APRIL 2004 161 Global Output Convergence of a Class of Continuous-Time Recurrent Neural Networks With Time-Varying Thresholds
More informationOn Detectability Of Networked Discrete Event Systems
Wayne State University Wayne State University Dissertations 1-1-2017 On Detectability Of Networked Discrete Event Systems Yazeed Sasi Wayne State University, Follow this and additional works at: http://digitalcommons.wayne.edu/oa_dissertations
More informationIntegrated Fault Diagnosis Based on Petri Net Models
16th IEEE International Conference on Control Applications Part of IEEE Multi-conference on Systems and Control Singapore, 1-3 October 2007 TuC05.3 Integrated Fault Diagnosis Based on Petri Net Models
More informationDecentralized Control of Discrete Event Systems with Multiple Local Specializations 1
Decentralized Control of Discrete Event Systems with Multiple Local Specializations Shengbing Jiang, Vigyan Chandra, Ratnesh Kumar Department of Electrical Engineering University of Kentucky Lexington,
More informationDiagnosability of Fuzzy Discrete Event Systems
DIAGNOSABILITY OF FUZZY DISCRETE EVENT SYSTEMS 1 Diagnosability of Fuzzy Discrete Event Systems Fuchun Liu a,b, Daowen Qiu a, Hongyan Xing a,b, and Zhujun Fan a a Department of Computer Science, Zhongshan
More informationSupervisory Control: Advanced Theory and Applications
Supervisory Control: Advanced Theory and Applications Dr Rong Su S1-B1b-59, School of EEE Nanyang Technological University Tel: +65 6790-6042, Email: rsu@ntu.edu.sg EE6226, Discrete Event Systems 1 Introduction
More informationAn Active Learning Approach For Inferring Discrete Event Automata
An Active Learning Approach For Inferring Discrete Event Automata Mohammad Mahdi Karimi PhD. Candidate, ECE Supervisor: Dr Ali Karimoddini Summer 2015 1 Content 1. Discrete Event Systems Definitions Applications
More informationA. Disjunctive Prognosers
2009 American Control Conference Hyatt Regency Riverfront, St. Louis, MO, USA June 10-12, 2009 FrB11.4 Multi-Decision Decentralized Prognosis of Failures in Discrete Event Systems Ahmed Khoumsi and Hicham
More informationIntroduction to Formal Languages, Automata and Computability p.1/51
Introduction to Formal Languages, Automata and Computability Finite State Automata K. Krithivasan and R. Rama Introduction to Formal Languages, Automata and Computability p.1/51 Introduction As another
More informationFeng Lin. Abstract. Inspired by thewell-known motto of Henry David Thoreau [1], that government
That Supervisor Is Best Which Supervises Least Feng Lin Department of Electrical and Computer Engineering Wayne State University, Detroit, MI 48202 Abstract Inspired by thewell-known motto of Henry David
More informationColoured Petri Nets Based Diagnosis on Causal Models
Coloured Petri Nets Based Diagnosis on Causal Models Soumia Mancer and Hammadi Bennoui Computer science department, LINFI Lab. University of Biskra, Algeria mancer.soumia@gmail.com, bennoui@gmail.com Abstract.
More informationDiagnosis of Repeated/Intermittent Failures in Discrete Event Systems
Diagnosis of Repeated/Intermittent Failures in Discrete Event Systems Shengbing Jiang, Ratnesh Kumar, and Humberto E. Garcia Abstract We introduce the notion of repeated failure diagnosability for diagnosing
More informationKeywords: Kalman Filter, Dual Kalman Filter, Battery Management System, state estimation, SOC, SOH
Functionality and Behaviour of an Dual Kalman Filter implemented on a Modular Battery-Management-System Conference on Future Automotive Technology: Focus Electromobility Georg Walder 1, Christian Campestrini
More informationControl Synthesis of Discrete Manufacturing Systems using Timed Finite Automata
Control Synthesis of Discrete Manufacturing Systems using Timed Finite utomata JROSLV FOGEL Institute of Informatics Slovak cademy of Sciences ratislav Dúbravská 9, SLOVK REPULIC bstract: - n application
More informationOnline Failure Diagnosis of Stochastic Discrete Event Systems
Online Failure iagnosis of Stochastic iscrete Event Systems Jun Chen, Student Member, IEEE and Ratnesh Kumar, Fellow, IEEE Abstract This paper deals with the detection of (permanent) fault in the setting
More informationMonitoring-based Diagnosis of Discrete-Event Systems with Uncertain Observations
Monitoring-based Diagnosis of Discrete-Event Systems with Uncertain Observations Gianfranco Lamperti Marina Zanella Dipartimento di Elettronica per l Automazione Università di Brescia, Italy lamperti@ing.unibs.it
More informationUNIT-II. NONDETERMINISTIC FINITE AUTOMATA WITH ε TRANSITIONS: SIGNIFICANCE. Use of ε-transitions. s t a r t. ε r. e g u l a r
Syllabus R9 Regulation UNIT-II NONDETERMINISTIC FINITE AUTOMATA WITH ε TRANSITIONS: In the automata theory, a nondeterministic finite automaton (NFA) or nondeterministic finite state machine is a finite
More informationRobust Supervisory Control of a Spacecraft Propulsion System
1 Robust Supervisory Control of a Spacecraft Propulsion System Farid Yari, Shahin Hashtrudi-Zad*, and Siamak Tafazoli In this paper the theory of supervisory control of discrete-event systems is used to
More informationAuthor's personal copy
Automatica 46 (2010) 1165 1175 Contents lists available at ScienceDirect Automatica journal homepage: www.elsevier.com/locate/automatica Optimal sensor activation for diagnosing discrete event systems
More informationFAULT diagnosis is crucial for ensuring the safe operation
A Qualitative Event-based Approach to Continuous Systems Diagnosis Matthew J. Daigle Member, IEEE, Xenofon D. Koutsoukos Senior Member, IEEE, and Gautam Biswas Senior Member, IEEE Abstract Fault diagnosis
More informationNondeterministic Finite Automata
Nondeterministic Finite Automata Not A DFA Does not have exactly one transition from every state on every symbol: Two transitions from q 0 on a No transition from q 1 (on either a or b) Though not a DFA,
More informationOVER THE past 20 years, the control of mobile robots has
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 18, NO. 5, SEPTEMBER 2010 1199 A Simple Adaptive Control Approach for Trajectory Tracking of Electrically Driven Nonholonomic Mobile Robots Bong Seok
More informationHybrid automaton incremental construction for online diagnosis
Hybrid automaton incremental construction for online diagnosis Jorge Vento, Louise Travé-Massuyès 2,3, Ramon Sarrate and Vicenç Puig Advanced Control Systems (SAC), Universitat Politècnica de Catalunya
More informationA Scalable Jointree Algorithm for Diagnosability
A Scalable Jointree Algorithm for Diagnosability Anika Schumann Advanced Computing Research Centre University of South Australia Mawson Lakes, SA 5095, Australia anika.schumann@cs.unisa.edu.au Jinbo Huang
More informationDictionary-Less Defect Diagnosis as Surrogate Single Stuck-At Faults
Dictionary-Less Defect Diagnosis as Surrogate Single Stuck-At Faults Chidambaram Alagappan and Vishwani D. Agrawal Department of Electrical and Computer Engineering Auburn University, Auburn, AL 36849,
More informationDecentralized Failure Diagnosis of Stochastic Discrete Event Systems
Decentralized Failure Diagnosis of Stochastic Discrete Event Systems Jun Chen, Student Member, IEEE and Ratnesh Kumar, Fellow, IEEE Abstract In decentralized diagnosis the system behavior is monitored
More informationResearch of High Voltage Intelligent Capacitor
International Conference on Computer Science and Intelligent Communication (CSIC 2015) Research of High Voltage Intelligent Capacitor Yang Minxiang, Song Wei, Zhao Xuesong Jiebei Electric Power Maintenance
More informationComments and Corrections
1386 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL 59, NO 5, MAY 2014 Comments and Corrections Corrections to Stochastic Barbalat s Lemma and Its Applications Xin Yu and Zhaojing Wu Abstract The proof of
More informationSupervisory Control of Hybrid Systems
X.D. Koutsoukos, P.J. Antsaklis, J.A. Stiver and M.D. Lemmon, "Supervisory Control of Hybrid Systems, in Special Issue on Hybrid Systems: Theory and Applications, Proceedings of the IEEE, P.J. Antsaklis,
More informationWhat You Must Remember When Processing Data Words
What You Must Remember When Processing Data Words Michael Benedikt, Clemens Ley, and Gabriele Puppis Oxford University Computing Laboratory, Park Rd, Oxford OX13QD UK Abstract. We provide a Myhill-Nerode-like
More informationDISTURBANCE LOAD MODELLING WITH EQUIVALENT VOLTAGE SOURCE METHOD IN GRID HARMONIC ASSESSMENT
DISTURBANCE LOAD MODELLING WITH EQUIVALENT VOLTAGE SOURCE METHOD IN GRID HARMONIC ASSESSMENT Xavier YANG Xingyan NIU Bruno PASZKIER EDF R&D France EDF R&D China EDF R&D - France xavier.yang@edf.fr xingyan.niu@edf.fr
More informationSynthesis of Maximally Permissive Non-blocking Supervisors for Partially Observed Discrete Event Systems
53rd IEEE Conference on Decision and Control December 5-7, 24. Los Angeles, California, USA Synthesis of Maximally Permissive Non-blocking Supervisors for Partially Observed Discrete Event Systems Xiang
More informationComputational Models #1
Computational Models #1 Handout Mode Nachum Dershowitz & Yishay Mansour March 13-15, 2017 Nachum Dershowitz & Yishay Mansour Computational Models #1 March 13-15, 2017 1 / 41 Lecture Outline I Motivation
More informationCONTROL AND DEADLOCK RECOVERY OF TIMED PETRI NETS USING OBSERVERS
5 e Conférence Francophone de MOdélisation et SIMulation Modélisation et simulation pour l analyse et l optimisation des systèmes industriels et logistiques MOSIM 04 du 1 er au 3 septembre 2004 - Nantes
More informationA Sliding Mode Control based on Nonlinear Disturbance Observer for the Mobile Manipulator
International Core Journal of Engineering Vol.3 No.6 7 ISSN: 44-895 A Sliding Mode Control based on Nonlinear Disturbance Observer for the Mobile Manipulator Yanna Si Information Engineering College Henan
More informationLecture 4 Nondeterministic Finite Accepters
Lecture 4 Nondeterministic Finite Accepters COT 4420 Theory of Computation Section 2.2, 2.3 Nondeterminism A nondeterministic finite automaton can go to several states at once. Transitions from one state
More informationCoordinated Decentralized Protocols for Failure Diagnosis of Discrete Event Systems
Discrete Event Dynamic Systems: Theory and Applications, 10, 33 86 (2000) c 2000 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands. Coordinated Decentralized Protocols for Failure Diagnosis
More informationComparing diagnosability in Continuous and Discrete-Event Systems
Comparing diagnosability in Continuous and Discrete-Event Systems Marie-Odile Cordier IRISA, Université de Rennes 1 Rennes, France Louise Travé-Massuyès and Xavier Pucel LAAS-CNRS Toulouse, France Abstract
More informationarxiv: v1 [math.oc] 21 Feb 2018
Noname manuscript No. (will be inserted by the editor) On detectability of labeled Petri nets with inhibitor arcs Kuize Zhang Alessandro Giua arxiv:1802.07551v1 [math.oc] 21 Feb 2018 Received: date / Accepted:
More informationReliability of Bulk Power Systems (cont d)
Reliability of Bulk Power Systems (cont d) Important requirements of a reliable electric power service Voltage and frequency must be held within close tolerances Synchronous generators must be kept running
More informationAutomata and Languages
Automata and Languages Prof. Mohamed Hamada Software Engineering Lab. The University of Aizu Japan Nondeterministic Finite Automata with empty moves (-NFA) Definition A nondeterministic finite automaton
More informationLet us first give some intuitive idea about a state of a system and state transitions before describing finite automata.
Finite Automata Automata (singular: automation) are a particularly simple, but useful, model of computation. They were initially proposed as a simple model for the behavior of neurons. The concept of a
More informationSupervisory Control of Petri Nets with. Uncontrollable/Unobservable Transitions. John O. Moody and Panos J. Antsaklis
Supervisory Control of Petri Nets with Uncontrollable/Unobservable Transitions John O. Moody and Panos J. Antsaklis Department of Electrical Engineering University of Notre Dame, Notre Dame, IN 46556 USA
More informationFault Diagnosis in Discrete-Event Systems: Incomplete Models and Learning
2005 American Control Conference June 8-10, 2005. Portland, OR, USA ThC15.5 ault Diagnosis in Discrete-Event Systems: Incomplete Models and Learning David L. Yeung School of Computer Science University
More informationApproximation Metrics for Discrete and Continuous Systems
University of Pennsylvania ScholarlyCommons Departmental Papers (CIS) Department of Computer & Information Science May 2007 Approximation Metrics for Discrete Continuous Systems Antoine Girard University
More informationMasked Prioritized Synchronization for Interaction and Control of Discrete Event Systems
Masked Prioritized Synchronization for Interaction and Control of Discrete Event Systems Ratnesh Kumar Department of Electrical Engineering University of Kentucky Lexington, KY 40506-0046 Michael Heymann
More informationElectrical Circuits. Winchester College Physics. makptb. c D. Common Time man. 3rd year Revision Test
Name... Set... Don.... manner~ man makptb Winchester College Physics 3rd year Revision Test Electrical Circuits Common Time 2011 Mark multiple choice answers with a cross (X) using the box below. I A B
More informationAttack-Resilient Supervisory Control of Discrete-Event Systems
1 Attack-Resilient Supervisory Control of Discrete-Event Systems Yu Wang, Alper Kamil Bozkurt and Miroslav Pajic arxiv:194.3264v1 [cs.fl] 5 Apr 219 Abstract In this work, we study the problem of supervisory
More informationAngle-Sensorless Zero- and Low-Speed Control of Bearingless Machines
216 IEEE IEEE Transactions on Magnetics, Vol. 52, No. 7, July 216 Angle-Sensorless Zero- and Low-Speed Control of Bearingless Machines T. Wellerdieck, T. Nussbaumer, J. W. Kolar This material is published
More informationExtension based Limited Lookahead Supervision of Discrete Event Systems
Extension based Limited Lookahead Supervision of Discrete Event Systems Ratnesh Kumar, Hok M. Cheung Department of Electrical Engineering University of Kentucky, Lexington, KY 40506 Steven I. Marcus Department
More informationFault Tolerant Controllability
2015 American Control Conference Palmer House Hilton July 1-3, 2015. Chicago, IL, USA Fault Tolerant Controllability Simon Radel, Aos Mulahuwaish, and Ryan J. Leduc Abstract In this paper we investigate
More informationON DIAGNOSIS AND PREDICTABILITY OF PARTIALLY-OBSERVED DISCRETE-EVENT SYSTEMS
ON DIAGNOSIS AND PREDICTABILITY OF PARTIALLY-OBSERVED DISCRETE-EVENT SYSTEMS by Sahika Genc A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Electrical
More informationSuperconducting Fault Current Limiter in DC Systems with MLI Fed to IM
Superconducting Fault Current Limiter in DC Systems with MLI Fed to IM 1 Rama Rao P.V.V., 2 M.Swathi 1,2 Shri Vishnu Engineering for Women, Bhimavaram, India Abstract: In this paper, an application of
More informationScalable Diagnosability Checking of Event-Driven Systems
Scalable Diagnosability Checking of Event-Driven Systems Anika Schumann The Australian National University, National ICT Australia anika.schumann@anu.edu.au Yannick Pencolé National Center for Scientific
More informationUses of finite automata
Chapter 2 :Finite Automata 2.1 Finite Automata Automata are computational devices to solve language recognition problems. Language recognition problem is to determine whether a word belongs to a language.
More informationLyapunov Stability of Linear Predictor Feedback for Distributed Input Delays
IEEE TRANSACTIONS ON AUTOMATIC CONTROL VOL. 56 NO. 3 MARCH 2011 655 Lyapunov Stability of Linear Predictor Feedback for Distributed Input Delays Nikolaos Bekiaris-Liberis Miroslav Krstic In this case system
More informationThe State Explosion Problem
The State Explosion Problem Martin Kot August 16, 2003 1 Introduction One from main approaches to checking correctness of a concurrent system are state space methods. They are suitable for automatic analysis
More informationIndustrial Automation (Automação de Processos Industriais)
Industrial Automation (Automação de Processos Industriais) Discrete Event Systems http://users.isr.ist.utl.pt/~jag/courses/api1516/api1516.html Slides 2010/2011 Prof. Paulo Jorge Oliveira Rev. 2011-2015
More informationClassification of Hand-Written Digits Using Scattering Convolutional Network
Mid-year Progress Report Classification of Hand-Written Digits Using Scattering Convolutional Network Dongmian Zou Advisor: Professor Radu Balan Co-Advisor: Dr. Maneesh Singh (SRI) Background Overview
More informationAbstraction-based synthesis: Challenges and victories
Abstraction-based synthesis: Challenges and victories Majid Zamani Hybrid Control Systems Group Electrical Engineering Department Technische Universität München December 14, 2015 Majid Zamani (TU München)
More informationAccurate Estimating Simultaneous Switching Noises by Using Application Specific Device Modeling
Accurate Estimating Simultaneous Switching Noises by Using Application Specific Device Modeling Li Ding and Pinaki Mazumder Department of Electrical Engineering and Computer Science The University of Michigan,
More informationComputability and Complexity
Computability and Complexity Non-determinism, Regular Expressions CAS 705 Ryszard Janicki Department of Computing and Software McMaster University Hamilton, Ontario, Canada janicki@mcmaster.ca Ryszard
More informationComputing running DCTs and DSTs based on their second-order shift properties
University of Wollongong Research Online Faculty of Informatics - Papers (Archive) Faculty of Engineering Information Sciences 000 Computing running DCTs DSTs based on their second-order shift properties
More information