Applying Particle Swarm Optimization to Adaptive Controller Leandro dos Santos Coelho 1 and Fabio A. Guerra 2
|
|
- Augustus Norris
- 5 years ago
- Views:
Transcription
1 Applying Particle Swarm Optimization to Adaptive Controller Leandro dos Santos Coelho 1 and Fabio A. Guerra 2 1 Production and Systems Engineering Graduate Program, PPGEPS Pontifical Catholic University of Parana, PUCPR Imaculada Conceição, 1155, Zip code , Curitiba, Parana, Brazil 2 Institute of Technology for Development, LACTEC Low Voltage Technology Unit, UTBT Centro Politécnico UFPR, Zip code , Curitiba, Parana, Brazil Abstract A design for a model-free learning adaptive control (MFLAC) based on pseudo-gradient concepts and optimization procedure by particle swarm optimization (PSO) is presented in this paper. PSO is a method for optimizing hard numerical functions on metaphor of social behavior of flocks of birds and schools of fish. A swarm consists of individuals, called particles, which change their positions over time. Each particle represents a potential solution to the problem. In a PSO system, particles fly around in a multi-dimensional search space. During its flight each particle adjusts its position according to its own experience and the experience of its neighboring particles, making use of the best position encountered by itself and its neighbors. The performance of each particle is measured according to a pre-defined fitness function, which is related to the problem being solved. The PSO has been found to be robust and fast in solving non-linear, non-differentiable, multi-modal problems. Motivation for application of PSO approach is to overcome the limitation of the conventional MFLAC design, which cannot guarantee satisfactory control performance when the plant has different gains for the operational range when designed by trial-and-error by user. Numerical results of the MFLAC with particle swarm optimization for a nonlinear control valve are showed. Keywords: particle swarm optimization, adaptive control, model-free adaptive control. Introduction Model-based control techniques are usually implemented under the assumption of good understanding of process dynamics and their operational environment. These techniques, however, cannot provide satisfactory results when applied to poorly modeled processes, which can operate in ill-defined environments. This is often the case when dealing with complex dynamic systems for which the physical processes are either highly nonlinear or are not fully understood [1]. The conventional proportional-integral-derivative (PID) algorithm is still widely used in process industries because its simplicity and robustness. PID controllers are the most common controllers in industry. In fact, 95% of control loops use PID and the majority is PI control [2]. However, its performance is not adequate in many chemical processes. A change in the signal and the
2 2 Leandro dos Santos Coelho1 and Fabio A. Guerra2 directionality of the process gain is a complex practical situation and, so, still becoming complex the design of a control system [3]. In addition, several approaches have been proposed in the literature for controlling nonlinear processes, such as model predictive control, neural control, fuzzy control, robust control, sliding mode control, and adaptive control. The aim of this paper is to merge for nonlinear systems, the model-free learning adaptive control structure [4], [5] with the controller design optimization based on particle swarm optimization (PSO) [6]. PSO methods explore the search space using a population of particles, each with a particle or agent, starting from a random location and velocity vector. Each particle in the swarm represents a candidate solution (treated as a point) in an n- dimensional space for the optimization problem, which adjusts its own flying according to other particles. Several heuristics have been developed in recent years to improve the performance and set up the parameters of the PSO algorithm [7]-[11]. Model-free learning adaptive control In this paper, the direct adaptive control of the following general discrete SISO (Single-Input and Single-Output) nonlinear system is considered ( ) y(k + ) = f y(, L,y(k n ),, L,k n ) (1) 1 a b where n a and n b are the orders of system output, y(, and input,, respectively, and f( ) is a general nonlinear function. The plant (equation 1) can be rewritten as follows: ( Y (,, U ( 1) ) y ( k + 1) = f k (2) where Y( and U(k-1) are the sets of system outputs and inputs up to sampling instant k and k-1. The following assumptions are considered about the controlled plant: (A1) the system (1) and (2) is observable and controllable; (A2) the partial derivative of f( ) with respect to control input is continuous; and (A3) the system (1) is generalized Lipschitz. For a nonlinear system (2), satisfying assumptions (A1-A3), then there must exist φ ( k ), called pseudo-gradient vector, when control change k ) 0, and T y( k + 1 ) = φ ( k ) k ) (3)
3 3 where the control change = - k-1); φ ( L, and L is a constant. Details of the theoretical basis and the mathematical proof of the MFLAC are y ( k + 1) = f Y (,, U ( k 1) given in [4] and [5]. In this proof, the equation ( ) gives or ( Y (,, U ( ) f ( Y (,, U ( k 2) ) y ( k + 1) = f (4) f y( k + 1) = f ( Y (,, U ( ) f ( Y (,, U ( ) ( Y (,, U ( ) f ( Y (,, U ( k 2) ) + (5) Using assumption (A2) and the mean value theorem, equation (5) gives y ( k f + 1 ) = k ) + ξ( k ) (6) k ) where f k ) denotes the value of gradient vector of f ( Y( k ), k ),U( k 1) ) with respect to u at some point between k 1) and u ( k ), and ξ ( k ) given by ( Y (,, U ( ) f ( Y (,, U ( k 2) ) ξ( k ) = f (7) Considering the following equation ξ ( k ) = η T ( k ) k ) (8) where η ( k ) is a variable. Since condition k ) 0, equation (8) must have solution η ( k ). f Let φ ( k ) = + η( k ) (9) k ) T From (8) and (9), then (7) can be rewritten as y( k + 1 ) = φ ( k ) k ). This is the same as (3). In this case, by using (3) and assumption (A3), and k ) 0, we have T φ ( k ) k ) L k ) (10) Hence φ ( k ) L.
4 4 Leandro dos Santos Coelho1 and Fabio A. Guerra2 For the learning control law algorithm, a weighted one-step-ahead control input cost function is adopted, and given by [ 1 1 ] 2 2 J() = y(k + ) y (k + ) +λ (11) For the control design, where y r (k+1) is the expected system output signal (true output of the controlled plant), and λ is a positive weighted constant. The equation (3) can be rewrite as follows y( k r T +1 ) = y( k ) + φ ( k ) k ) (12) Substituting (12) into (11), differentiating (11) with respect to, solving the equation J ( k )) / k ) = 0, and using the matrix-inversion-lemma gives the control law as follows: ρφ k ( k ) = k 1) + y 1 2 r( k + ) y( k ) λ+ φ( [ ] (13) The control law (13) is a kind of control that has no relationship with any structural information (mathematical model, order, structure, etc.) of the controlled plant. It is designed only using I/O data of the plant. The cost function proposed by Hou et al. [5] for parameter estimation is used in this paper as T 2 2 [ y( y( φ ] + µ φ( φˆ( k 1) J ( φ( ) = (14) Using the similar procedure of control law equations, we can obtain the parameter estimation algorithm as follows: η φˆ( = φˆ( + 2 µ + [ ˆT y( φ ( ] (15) Summarizing, the MFLAC scheme is η φˆ( = φˆ( + 2 µ + [ ˆT y( φ ( ] (16) ˆφ ( k ) = ˆ φ( 1) if
5 5 sign( φ( 1 )) sign( ˆ φ( k )) (17) ˆφ ( k ) = ˆ φ( 1) if ˆ φ( k ) M, or ˆ( φ ε (18) ρφ k ( k ) = k 1) + y 1 2 r( k + ) y( k ) λ+ φ( [ ] (19) where step-size series ρ and η, and the weighted constants λ and µ are design parameters optimized by differential evolution in this paper. The parameter ε is a small positive constant (adopted ), M is adopted with value 10, and ˆφ ( k ) = ˆ φ( 1) is the initial estimation value of φ( k ). Optimization using PSO The proposal of PSO algorithm was put forward by several scientists who developed computational simulations of the movement of organisms such as flocks of birds and schools of fish. Such simulations were heavily based on manipulating the distances between individuals, i.e., the synchrony of the behavior of the swarm was seen as an effort to keep an optimal distance between them. Sociobiologist Edward Osbourne Wilson outlined a link of these simulations for optimization problems [6]. PSO, originally developed by Kennedy and Eberhart in 1995, is a populationbased swarm algorithm [12], [13]. In the PSO computational algorithm, population dynamics simulates bio-inspired behavior, i.e., a bird flock s behavior which involves social sharing of information and allows particles to to take profit from the discoveries and previous experience of all the other particles during the search for food. Each particle in PSO has a randomized velocity associated to it, which moves through the problem space. Each particle in PSO keeps track of its coordinates in the problem space, which are associated with the best solution (fitness) it has achieved so far. This value is called pbest (personal best). Another best value that is tracked by the global version of the particle swarm optimizer is the overall best value. Its location, called gbest (global best), is obtained by any particle in the population. The past best position and the entire best overall position of the group are employed to minimize (or maximize) the solution The PSO concept consists, in each time step, of changing the velocity (acceleration) of each particle flying toward its pbest and gbest locations (global version of PSO). Acceleration is weighted by random terms, with separate random
6 6 Leandro dos Santos Coelho1 and Fabio A. Guerra2 numbers being generated for acceleration toward pbest and gbest locations, respectively. The procedure for implementing the global version of PSO is given by the following steps: Step 1: Initialization random swarm positions and velocities: Initialize a population (array) of particles with random positions and velocities in the n dimensional problem space using uniform probability distribution function. Step 2: Evaluation of particle s fitness: Evaluate each particle s fitness value. Step 3: Comparison to pbest (personal best): Compare each particle s fitness with the particle s pbest. If the current value is better than pbest, then set the pbest value equal to the current value and the pbest location equal to the current location in n-dimensional space. Step 3: Comparison to gbest (global best): Compare the fitness with the population s overall previous best. If the current value is better than gbest, then reset gbest to the current particle s array index and value. Step 4: Updating of a particle s velocity and position: Change the velocity, v i, and position of the particle, x i, according to equations (20) and (21): v t + 1) = w v ( t) + c ud ( t) [ p ( t) x ( t)] + c Ud ( t) [ p ( t) x ( )] (20) i( i 1 i i i 2 i g i t x (t + 1 ) = x (t ) + t v(t + ) (21) i i i 1 where i=1,2,,n indicates the number of particles of population (swarm); t=1,2, t max, indicates the iterations, w is a parameter called the inertial weight; = [ v,v,..., v ] T stands for the velocity of the i-th particle, = [ x,x,..., x ] T vi i1 i2 in xi i1 i2 in stands for the position of the i-th particle of population, and [ p, p,..., p ] T pi = i1 i2 in represents the best previous position of the i-th particle. Positive constants c 1 and c 2 are the cognitive and social components, respectively, which are the acceleration constants responsible for varying the particle speed towards pbest and gbest, respectively. Index g represents the index of the best particle among all the particles in the swarm. Variables ud i (t) and Ud i (t) are two random functions in the range [0,1]. Equation (1) represents the position update, according to its previous position and its velocity, considering t = 1. Step 5. Repeating the evolutionary cycle: Return to step (ii) until a stop criterion is met, usually a sufficiently good fitness or a maximum number of iterations (generations). In this work, a time-varying modification of c 1 and c 2 was used that can be represented as follows [14]:
7 7 t c1 = (c1 f c1i ) + c1i (22) tmax t c2 = (c2 f c2i ) + c2i (23) tmax where c 1i, c 1f, c 2i and c 2f are constants. In this work, an improved solution based on preliminary tests was observed when changing c 1 from 2.05 to 0.4 and changing c 2 from 0.4 to 2.5, i.e., the values c 1i = 2.05, c 1f = 0.4, c 2i = 0.4 and c 2f = 2.05 were adopted in the simulations done here. The inertial weight w represents the degree of the momentum of the particles. The use of the variable w, inertial weight, is responsible for dynamically adjusting the speed of the particles. The velocity of i-th particles in each dimension is associated with a maximum velocity V max. If the sum of accelerations causes the velocity in that dimension to exceed V max, which is a parameter specified by the user, then the velocity in that dimension is limited to V max. The parameter, V max, is used to determine the resolution with which the regions around the current solutions are searched. If V max is too high, the PSO facilitates global search, and particles may fly past good solutions; if it is too small, the PSO facilitates local search, and the particles may not explore sufficiently beyond locally good regions. The choice of the PSO approach for optimization of MFLAC design is based on its useful features such as [11]: (i) it is a stochastic search algorithm that is originally motivated by the mechanisms of swarm intelligence, (ii) it is less likely become trapped in a local optimum because it searches for the global optimal solution by manipulating a population of candidate solutions, and (iii) it is very effective for solving the optimization problems with nonsmooth objective functions as it does not require the derivative information. In this paper, a PSO-based optimization technique is adopted to obtain φ(1), ρ, η, λ and µ for the MFLAC design. The setup of PSO used in this work was the following: number of particles (swarm population size): 30; inertial weight using a linear reduction equation with initial and final values of 0.7 and 0.4, respectively; stop criterion: 20 generations. The objective of the PSO in the MFLAC optimization is to maximize the fitness equation given by
8 8 Leandro dos Santos Coelho1 and Fabio A. Guerra2 f ξ = t 1 + y( yr ( i= 1 [ k 1) ] 2 (24) where is the control signal, y( is the process output, and y r ( is the reference (setpoint), and ξ is a scale factor (adopted ξ = 0.3). Simulation results The control valve system is an opening with adjustable area. Normally it consists of an actuator, a valve body and a valve plug. The actuator is a device that transforms the control signal to movement of the stem and valve plug. Wigren [15] describes the plant where the control valve dynamic is described by a Wiener model (the nonlinear element follows linear bloc and it is given by x( = 1,5714x( + 0,6873x( k 2) + 0,0616k-1 ) + 0,0543k-2 ) (25) x( y( = fn[ x( ] = (26) 0,10 + 0,90 [ x( ] 2 where is the control pressure, x( is the stem position, and y( is the flow through the valve which is the controlled variable. The input to the process,, is constrained between [0; 1.2]. The nonlinear behavior of the control valve described by equation (26) is shown in Figure 1. Figure 1. Static characteristic of a control valve. The space search adopted in PSO setup is: 0.01 φ (1) 0. 50, 0.10 ρ 5.00, 1.00 η 1. 00, 0.01 λ 1. 00, and 1.00 µ
9 9 For the MFLAC design, the optimization procedure by PSO obtains φ ( 1) = , ρ = , η = , λ = , µ = and fitness f = (best results in 30 runs). Simulation results for servo and regulatory responses of MFLAC are shown in Figures 2 and 3, respectively. Regulatory behavior analysis of the MFLAC was based on parametric changes in the plant output when: (i) sample 60: y( = y( + 0.2; (ii) sample 160: y( = y( - 0.2; (iii) sample 260: y( = y( 0.4; (iv) sample 360: y( = y( + 0.4; and (v) sample 460: y( = y( Numerical results presented in Figures 2 and 3 show that the MFLAC using PSO approach have precise control performance. In Table 1, a summary of simulation results and performance of the MFLAC design based on PSO is presented. Table 1. Indices for the best MFLAC design using PSO. MFLAC servo behavior regulatory behavior mean of u variance of u mean of error variance of error Figure 2. Input and output signals for the MFLAC (servo behavior). Figure 3. Input and output signals for the MFLAC (regulatory behavior).
10 10 Leandro dos Santos Coelho1 and Fabio A. Guerra2 Conclusion and future research Numerical results for controlling a control valve have shown the efficiency of the proposed MFLAC that guaranteed the convergence of the tracking error for servo and regulatory responses. However, it still has a distance to industrial applications and more practical issues must be done. A further investigation can be directed to analyze the PSO for model-free adaptive control methods [16] in essential control issues such as control performance, robustness and stability. References [1] F. Karray, W. Gueaieb, and S. Al-Sharhan, The hierarchical expert tuning of pid controllers using tools of soft computing, IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, vol. 32, no. 1, pp , [2] K. J. Åström and T. Hägglund, PID controllers: theory, design, and tuning. Instrument Society of America, ISA, [3] B. H. Bisowarno, Y. -C. Tian, and M. O. Tade, Model gain scheduling control of an ethyl tertbutyl ether reactive distillation column, Ind. Eng. Chem. Res., vol. 42, pp , [4] Z. Hou and W. Huang, The model-free learning adaptive control of a class of siso nonlinear systems, Proceedings of the American Control Conference, Albuquerque, NM, pp , [5] Z. Hou, C. Han, and W. Huang, The model-free learning adaptive control of a class of MISO nonlinear discrete-time systems, IFAC Low Cost Automation, Shenyang, P. R. China, pp , [6] J. F. Kennedy, R. C. Eberhart and R. C. Shi, Swarm intelligence, Morgan Kaufmann Pub, San Francisco, USA, [7] Y. Shi and R. C. Eberhart, Parameter selection in PSO optimization, Proceedings of the 7th Annual Conf. Evolutionary Programming, San Diego, CA, USA, pp , [8] K. Yasuda, A. Ide, and N. Iwasaki, Adaptive particle swarm optimization, Proceedings of IEEE Int. Conf. on Systems, Man and Cybernetics, Washington, DC, USA, vol. 2, pp , [9] D. Devicharan and C. K. Mohan, Particle swarm optimization with adaptive linkage learning, Proceedings of the IEEE Congress on Evol. Computation, Portland, OR, USA, , [10] R. Mendes and J. F. Kennedy, The fully informed particle swarm: simper, maybe better, IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp , [11] R. A. Krohling, F. Hoffmann, and L. S. Coelho, Co-evolutionary particle swarm optimization for min-max problems using Gaussian distribution, Proceedings of Congress on Evolutionary Computation, Portland, USA, , [12] J. F. Kennedy and R. C. Eberhart, Particle swarm optimization, Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, pp , [13] R. C. Eberhart and J. F. Kennedy, A new optimizer using particle swarm theory, Proceedings of International Symposium on Micro Machine and Human Science, Japan, pp , [14] A. Ratnaweera, S. K. Halgamuge, and H. C. Watson, Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp , [15] T. Wigren, Recursive prediction error identification using the nonlinear Wiener model, Automatica, vol. 29, no. 4, pp , [16] J. C. Spall and J. A. Cristion, Model-free control of nonlinear systems with discrete time measurements, IEEE Transactions on Automatic Control, vol. 43, pp , 1998.
OPTIMIZATION OF MODEL-FREE ADAPTIVE CONTROLLER USING DIFFERENTIAL EVOLUTION METHOD
ABCM Symposium Series in Mechatronics - Vol. 3 - pp.37-45 Copyright c 2008 by ABCM OPTIMIZATION OF MODEL-FREE ADAPTIVE CONTROLLER USING DIFFERENTIAL EVOLUTION METHOD Leandro dos Santos Coelho Industrial
More informationOPTIMAL DISPATCH OF REAL POWER GENERATION USING PARTICLE SWARM OPTIMIZATION: A CASE STUDY OF EGBIN THERMAL STATION
OPTIMAL DISPATCH OF REAL POWER GENERATION USING PARTICLE SWARM OPTIMIZATION: A CASE STUDY OF EGBIN THERMAL STATION Onah C. O. 1, Agber J. U. 2 and Ikule F. T. 3 1, 2, 3 Department of Electrical and Electronics
More informationA Particle Swarm Optimization (PSO) Primer
A Particle Swarm Optimization (PSO) Primer With Applications Brian Birge Overview Introduction Theory Applications Computational Intelligence Summary Introduction Subset of Evolutionary Computation Genetic
More informationA Method of HVAC Process Object Identification Based on PSO
2017 3 45 313 doi 10.3969 j.issn.1673-7237.2017.03.004 a a b a. b. 201804 PID PID 2 TU831 A 1673-7237 2017 03-0019-05 A Method of HVAC Process Object Identification Based on PSO HOU Dan - lin a PAN Yi
More informationA Novel Approach for Complete Identification of Dynamic Fractional Order Systems Using Stochastic Optimization Algorithms and Fractional Calculus
5th International Conference on Electrical and Computer Engineering ICECE 2008, 20-22 December 2008, Dhaka, Bangladesh A Novel Approach for Complete Identification of Dynamic Fractional Order Systems Using
More informationA PSO Approach for Optimum Design of Multivariable PID Controller for nonlinear systems
A PSO Approach for Optimum Design of Multivariable PID Controller for nonlinear systems Taeib Adel Email: taeibadel@live.fr Ltaeif Ali Email: ltaief24@yahoo.fr Chaari Abdelkader Email: nabile.chaari@yahoo.fr
More informationFuzzy adaptive catfish particle swarm optimization
ORIGINAL RESEARCH Fuzzy adaptive catfish particle swarm optimization Li-Yeh Chuang, Sheng-Wei Tsai, Cheng-Hong Yang. Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan
More informationParticle Swarm Optimization. Abhishek Roy Friday Group Meeting Date:
Particle Swarm Optimization Abhishek Roy Friday Group Meeting Date: 05.25.2016 Cooperation example Basic Idea PSO is a robust stochastic optimization technique based on the movement and intelligence of
More informationON THE USE OF RANDOM VARIABLES IN PARTICLE SWARM OPTIMIZATIONS: A COMPARATIVE STUDY OF GAUSSIAN AND UNIFORM DISTRIBUTIONS
J. of Electromagn. Waves and Appl., Vol. 23, 711 721, 2009 ON THE USE OF RANDOM VARIABLES IN PARTICLE SWARM OPTIMIZATIONS: A COMPARATIVE STUDY OF GAUSSIAN AND UNIFORM DISTRIBUTIONS L. Zhang, F. Yang, and
More informationPARTICLE SWARM OPTIMISATION (PSO)
PARTICLE SWARM OPTIMISATION (PSO) Perry Brown Alexander Mathews Image: http://www.cs264.org/2009/projects/web/ding_yiyang/ding-robb/pso.jpg Introduction Concept first introduced by Kennedy and Eberhart
More informationParticle swarm optimization (PSO): a potentially useful tool for chemometrics?
Particle swarm optimization (PSO): a potentially useful tool for chemometrics? Federico Marini 1, Beata Walczak 2 1 Sapienza University of Rome, Rome, Italy 2 Silesian University, Katowice, Poland Rome,
More informationSimultaneous state and input estimation of non-linear process with unknown inputs using particle swarm optimization particle filter (PSO-PF) algorithm
Simultaneous state and input estimation of non-linear process with unknown inputs using particle swarm optimization particle filter (PSO-PF) algorithm Mohammad A. Khan, CSChe 2016 Outlines Motivations
More informationA self-guided Particle Swarm Optimization with Independent Dynamic Inertia Weights Setting on Each Particle
Appl. Math. Inf. Sci. 7, No. 2, 545-552 (2013) 545 Applied Mathematics & Information Sciences An International Journal A self-guided Particle Swarm Optimization with Independent Dynamic Inertia Weights
More informationOptimal Placement and Sizing of Distributed Generation for Power Loss Reduction using Particle Swarm Optimization
Available online at www.sciencedirect.com Energy Procedia 34 (2013 ) 307 317 10th Eco-Energy and Materials Science and Engineering (EMSES2012) Optimal Placement and Sizing of Distributed Generation for
More informationB-Positive Particle Swarm Optimization (B.P.S.O)
Int. J. Com. Net. Tech. 1, No. 2, 95-102 (2013) 95 International Journal of Computing and Network Technology http://dx.doi.org/10.12785/ijcnt/010201 B-Positive Particle Swarm Optimization (B.P.S.O) Muhammad
More informationBeta Damping Quantum Behaved Particle Swarm Optimization
Beta Damping Quantum Behaved Particle Swarm Optimization Tarek M. Elbarbary, Hesham A. Hefny, Atef abel Moneim Institute of Statistical Studies and Research, Cairo University, Giza, Egypt tareqbarbary@yahoo.com,
More informationA PSO APPROACH FOR PREVENTIVE MAINTENANCE SCHEDULING OPTIMIZATION
2009 International Nuclear Atlantic Conference - INAC 2009 Rio de Janeiro,RJ, Brazil, September27 to October 2, 2009 ASSOCIAÇÃO BRASILEIRA DE ENERGIA NUCLEAR - ABEN ISBN: 978-85-99141-03-8 A PSO APPROACH
More informationThree Steps toward Tuning the Coordinate Systems in Nature-Inspired Optimization Algorithms
Three Steps toward Tuning the Coordinate Systems in Nature-Inspired Optimization Algorithms Yong Wang and Zhi-Zhong Liu School of Information Science and Engineering Central South University ywang@csu.edu.cn
More informationThe Parameters Selection of PSO Algorithm influencing On performance of Fault Diagnosis
The Parameters Selection of Algorithm influencing On performance of Fault Diagnosis Yan HE,a, Wei Jin MA and Ji Ping ZHANG School of Mechanical Engineering and Power Engineer North University of China,
More informationThe particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters 85 (2003) 317 325 www.elsevier.com/locate/ipl The particle swarm optimization algorithm: convergence analysis and parameter selection Ioan Cristian Trelea INA P-G, UMR Génie
More informationThree Steps toward Tuning the Coordinate Systems in Nature-Inspired Optimization Algorithms
Three Steps toward Tuning the Coordinate Systems in Nature-Inspired Optimization Algorithms Yong Wang and Zhi-Zhong Liu School of Information Science and Engineering Central South University ywang@csu.edu.cn
More informationParticle swarm optimization approach to portfolio optimization
Nonlinear Analysis: Real World Applications 10 (2009) 2396 2406 Contents lists available at ScienceDirect Nonlinear Analysis: Real World Applications journal homepage: www.elsevier.com/locate/nonrwa Particle
More informationPVP2006-ICPVT
Proceedings of PVP 2006 / ICPVT- 2006 ASME Pressure Vessels and Piping/ICPVT- Conference July 23-27, 2006, Vancouver, BC, Canada PVP2006-ICPVT-9363 PARTICLE SWARM OPTIMIZATION (PSO) FUZZY SYSTEMS AND NARMAX
More informationSolving Numerical Optimization Problems by Simulating Particle-Wave Duality and Social Information Sharing
International Conference on Artificial Intelligence (IC-AI), Las Vegas, USA, 2002: 1163-1169 Solving Numerical Optimization Problems by Simulating Particle-Wave Duality and Social Information Sharing Xiao-Feng
More informationACTA UNIVERSITATIS APULENSIS No 11/2006
ACTA UNIVERSITATIS APULENSIS No /26 Proceedings of the International Conference on Theory and Application of Mathematics and Informatics ICTAMI 25 - Alba Iulia, Romania FAR FROM EQUILIBRIUM COMPUTATION
More informationOPTIMAL POWER FLOW BASED ON PARTICLE SWARM OPTIMIZATION
U.P.B. Sci. Bull., Series C, Vol. 78, Iss. 3, 2016 ISSN 2286-3540 OPTIMAL POWER FLOW BASED ON PARTICLE SWARM OPTIMIZATION Layth AL-BAHRANI 1, Virgil DUMBRAVA 2 Optimal Power Flow (OPF) is one of the most
More informationAbstract. 2. Dynamical model of power system
Optimization Of Controller Parametersfornon-Linear Power Systems Using Different Optimization Techniques Rekha 1,Amit Kumar 2, A. K. Singh 3 1, 2 Assistant Professor, Electrical Engg. Dept. NIT Jamshedpur
More informationGAIN SCHEDULING CONTROL WITH MULTI-LOOP PID FOR 2- DOF ARM ROBOT TRAJECTORY CONTROL
GAIN SCHEDULING CONTROL WITH MULTI-LOOP PID FOR 2- DOF ARM ROBOT TRAJECTORY CONTROL 1 KHALED M. HELAL, 2 MOSTAFA R.A. ATIA, 3 MOHAMED I. ABU EL-SEBAH 1, 2 Mechanical Engineering Department ARAB ACADEMY
More informationGREENHOUSE AIR TEMPERATURE CONTROL USING THE PARTICLE SWARM OPTIMISATION ALGORITHM
Copyright 00 IFAC 5th Triennial World Congress, Barcelona, Spain GREEHOUSE AIR TEMPERATURE COTROL USIG THE PARTICLE SWARM OPTIMISATIO ALGORITHM J.P. Coelho a, P.B. de Moura Oliveira b,c, J. Boaventura
More informationPower Electronic Circuits Design: A Particle Swarm Optimization Approach *
Power Electronic Circuits Design: A Particle Swarm Optimization Approach * Jun Zhang, Yuan Shi, and Zhi-hui Zhan ** Department of Computer Science, Sun Yat-sen University, China, 510275 junzhang@ieee.org
More informationCAPACITOR PLACEMENT USING FUZZY AND PARTICLE SWARM OPTIMIZATION METHOD FOR MAXIMUM ANNUAL SAVINGS
CAPACITOR PLACEMENT USING FUZZY AND PARTICLE SWARM OPTIMIZATION METHOD FOR MAXIMUM ANNUAL SAVINGS M. Damodar Reddy and V. C. Veera Reddy Department of Electrical and Electronics Engineering, S.V. University,
More informationDistributed Particle Swarm Optimization
Distributed Particle Swarm Optimization Salman Kahrobaee CSCE 990 Seminar Main Reference: A Comparative Study of Four Parallel and Distributed PSO Methods Leonardo VANNESCHI, Daniele CODECASA and Giancarlo
More informationApplication of Teaching Learning Based Optimization for Size and Location Determination of Distributed Generation in Radial Distribution System.
Application of Teaching Learning Based Optimization for Size and Location Determination of Distributed Generation in Radial Distribution System. Khyati Mistry Electrical Engineering Department. Sardar
More informationSecondary Frequency Control of Microgrids In Islanded Operation Mode and Its Optimum Regulation Based on the Particle Swarm Optimization Algorithm
International Academic Institute for Science and Technology International Academic Journal of Science and Engineering Vol. 3, No. 1, 2016, pp. 159-169. ISSN 2454-3896 International Academic Journal of
More informationGain Scheduling Control with Multi-loop PID for 2-DOF Arm Robot Trajectory Control
Gain Scheduling Control with Multi-loop PID for 2-DOF Arm Robot Trajectory Control Khaled M. Helal, 2 Mostafa R.A. Atia, 3 Mohamed I. Abu El-Sebah, 2 Mechanical Engineering Department ARAB ACADEMY FOR
More informationA Fast Method for Embattling Optimization of Ground-Based Radar Surveillance Network
A Fast Method for Embattling Optimization of Ground-Based Radar Surveillance Network JIANG Hai University of Chinese Academy of Sciences National Astronomical Observatories, Chinese Academy of Sciences
More informationTuning of Extended Kalman Filter for nonlinear State Estimation
OSR Journal of Computer Engineering (OSR-JCE) e-ssn: 78-0661,p-SSN: 78-877, Volume 18, ssue 5, Ver. V (Sep. - Oct. 016), PP 14-19 www.iosrjournals.org Tuning of Extended Kalman Filter for nonlinear State
More informationFuzzy Cognitive Maps Learning through Swarm Intelligence
Fuzzy Cognitive Maps Learning through Swarm Intelligence E.I. Papageorgiou,3, K.E. Parsopoulos 2,3, P.P. Groumpos,3, and M.N. Vrahatis 2,3 Department of Electrical and Computer Engineering, University
More informationLazy learning for control design
Lazy learning for control design Gianluca Bontempi, Mauro Birattari, Hugues Bersini Iridia - CP 94/6 Université Libre de Bruxelles 5 Bruxelles - Belgium email: {gbonte, mbiro, bersini}@ulb.ac.be Abstract.
More informationApplication of GA and PSO Tuned Fuzzy Controller for AGC of Three Area Thermal- Thermal-Hydro Power System
International Journal of Computer Theory and Engineering, Vol. 2, No. 2 April, 2 793-82 Application of GA and PSO Tuned Fuzzy Controller for AGC of Three Area Thermal- Thermal-Hydro Power System S. K.
More informationPSO with Adaptive Mutation and Inertia Weight and Its Application in Parameter Estimation of Dynamic Systems
Vol. 37, No. 5 ACTA AUTOMATICA SINICA May, 2011 PSO with Adaptive Mutation and Inertia Weight and Its Application in Parameter Estimation of Dynamic Systems ALFI Alireza 1 Abstract An important problem
More informationOptimization of PI Parameters for Speed Controller of a Permanent Magnet Synchronous Motor by using Particle Swarm Optimization Technique
Optimization of PI Parameters for Speed Controller of a Permanent Magnet Synchronous Motor by using Particle Swarm Optimization Technique Aiffah Mohammed 1, Wan Salha Saidon 1, Muhd Azri Abdul Razak 2,
More informationBinary Particle Swarm Optimization with Crossover Operation for Discrete Optimization
Binary Particle Swarm Optimization with Crossover Operation for Discrete Optimization Deepak Singh Raipur Institute of Technology Raipur, India Vikas Singh ABV- Indian Institute of Information Technology
More informationInternational Journal of Scientific & Engineering Research, Volume 8, Issue 1, January-2017 ISSN
ISSN 2229-5518 33 Voltage Regulation for a Photovoltaic System Connected to Grid by Using a Swarm Optimization Techniques Ass.prof. Dr.Mohamed Ebrahim El sayed Dept. of Electrical Engineering Al-Azhar
More informationOn Optimal Power Flow
On Optimal Power Flow K. C. Sravanthi #1, Dr. M. S. Krishnarayalu #2 # Department of Electrical and Electronics Engineering V R Siddhartha Engineering College, Vijayawada, AP, India Abstract-Optimal Power
More informationPerformance Comparison of PSO Based State Feedback Gain (K) Controller with LQR-PI and Integral Controller for Automatic Frequency Regulation
Performance Comparison of PSO Based Feedback Gain Controller with LQR-PI and Controller for Automatic Frequency Regulation NARESH KUMARI 1, A. N. JHA 2, NITIN MALIK 3 1,3 School of Engineering and Technology,
More informationPerformance Evaluation of IIR Filter Design Using Multi-Swarm PSO
Proceedings of APSIPA Annual Summit and Conference 2 6-9 December 2 Performance Evaluation of IIR Filter Design Using Multi-Swarm PSO Haruna Aimi and Kenji Suyama Tokyo Denki University, Tokyo, Japan Abstract
More informationDiscrete Evaluation and the Particle Swarm Algorithm.
Abstract Discrete Evaluation and the Particle Swarm Algorithm. Tim Hendtlass and Tom Rodgers, Centre for Intelligent Systems and Complex Processes, Swinburne University of Technology, P. O. Box 218 Hawthorn
More informationRegular paper. Particle Swarm Optimization Applied to the Economic Dispatch Problem
Rafik Labdani Linda Slimani Tarek Bouktir Electrical Engineering Department, Oum El Bouaghi University, 04000 Algeria. rlabdani@yahoo.fr J. Electrical Systems 2-2 (2006): 95-102 Regular paper Particle
More informationA New Improvement of Conventional PI/PD Controllers for Load Frequency Control With Scaled Fuzzy Controller
International Journal of Engineering and Applied Sciences (IJEAS) ISSN: 2394-3661, Volume-2, Issue-4, April 2015 A New Improvement of Conventional PI/PD Controllers for Load Frequency Control With Scaled
More informationPSO Based Predictive Nonlinear Automatic Generation Control
PSO Based Predictive Nonlinear Automatic Generation Control MUHAMMAD S. YOUSUF HUSSAIN N. AL-DUWAISH Department of Electrical Engineering ZAKARIYA M. AL-HAMOUZ King Fahd University of Petroleum & Minerals,
More informationDesign of Decentralised PI Controller using Model Reference Adaptive Control for Quadruple Tank Process
Design of Decentralised PI Controller using Model Reference Adaptive Control for Quadruple Tank Process D.Angeline Vijula #, Dr.N.Devarajan * # Electronics and Instrumentation Engineering Sri Ramakrishna
More informationDiscrete evaluation and the particle swarm algorithm
Volume 12 Discrete evaluation and the particle swarm algorithm Tim Hendtlass and Tom Rodgers Centre for Intelligent Systems and Complex Processes Swinburne University of Technology P. O. Box 218 Hawthorn
More informationArtificial Immune System Based DSTATCOM Control for an Electric Ship Power System
Artificial Immune System Based DSTATCOM Control for an Electric Ship Power System Pinaki Mitra and Ganesh K. Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory, Department of Electrical
More informationPROMPT PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER DISPATCH PROBLEM
PROMPT PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER DISPATCH PROBLEM K. Lenin 1 Research Scholar Jawaharlal Nehru Technological University Kukatpally,Hyderabad 500 085, India
More informationOptimal tunning of lead-lag and fuzzy logic power system stabilizers using particle swarm optimization
Available online at www.sciencedirect.com Expert Systems with Applications Expert Systems with Applications xxx (2008) xxx xxx www.elsevier.com/locate/eswa Optimal tunning of lead-lag and fuzzy logic power
More informationThe Essential Particle Swarm. James Kennedy Washington, DC
The Essential Particle Swarm James Kennedy Washington, DC Kennedy.Jim@gmail.com The Social Template Evolutionary algorithms Other useful adaptive processes in nature Social behavior Social psychology Looks
More informationAlgorithm for Multiple Model Adaptive Control Based on Input-Output Plant Model
BULGARIAN ACADEMY OF SCIENCES CYBERNEICS AND INFORMAION ECHNOLOGIES Volume No Sofia Algorithm for Multiple Model Adaptive Control Based on Input-Output Plant Model sonyo Slavov Department of Automatics
More informationLimiting the Velocity in the Particle Swarm Optimization Algorithm
Limiting the Velocity in the Particle Swarm Optimization Algorithm Julio Barrera 1, Osiris Álvarez-Bajo 2, Juan J. Flores 3, Carlos A. Coello Coello 4 1 Universidad Michoacana de San Nicolás de Hidalgo,
More informationStochastic Velocity Threshold Inspired by Evolutionary Programming
Stochastic Velocity Threshold Inspired by Evolutionary Programming Zhihua Cui Xingjuan Cai and Jianchao Zeng Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and
More informationNonlinear Process Identification Using Fuzzy Wavelet Neural Network Based on Particle Swarm Optimization Algorithm
J. Basic. Appl. Sci. Res., 3(5)30-309, 013 013, TextRoad Publication ISSN 090-4304 Journal of Basic and Applied Scientific Research www.textroad.com Nonlinear Process Identification Using Fuzzy Wavelet
More informationV-Formation as Optimal Control
V-Formation as Optimal Control Ashish Tiwari SRI International, Menlo Park, CA, USA BDA, July 25 th, 2016 Joint work with Junxing Yang, Radu Grosu, and Scott A. Smolka Outline Introduction The V-Formation
More informationController-Dynamic-Linearization-Based Model Free Adaptive Control for Discrete-Time Nonlinear Systems
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL 9, NO 4, NOVEMBER 2013 2301 Controller-Dynamic-Linearization-Based Model Free Adaptive Control for Discrete-Time Nonlinear Systems Zhongsheng Hou and Yuanming
More informationThe Efficiency of Particle Swarm Optimization Applied on Fuzzy Logic DC Motor Speed Control
SERBIAN JOURNAL OF ELECTRICAL ENGINEERING Vol. 5, No. 2, November 2008, 247-262 The Efficiency of Particle Swarm Optimization Applied on Fuzzy Logic DC Motor Speed Control Boumediene Allaoua 1, Abdessalam
More informationHybrid particle swarm algorithm for solving nonlinear constraint. optimization problem [5].
Hybrid particle swarm algorithm for solving nonlinear constraint optimization problems BINGQIN QIAO, XIAOMING CHANG Computers and Software College Taiyuan University of Technology Department of Economic
More informationARTIFICIAL INTELLIGENCE
BABEŞ-BOLYAI UNIVERSITY Faculty of Computer Science and Mathematics ARTIFICIAL INTELLIGENCE Solving search problems Informed local search strategies Nature-inspired algorithms March, 2017 2 Topics A. Short
More informationA NETWORK TRAFFIC PREDICTION MODEL BASED ON QUANTUM INSPIRED PSO AND WAVELET NEURAL NETWORK. Kun Zhang
Mathematical and Computational Applications, Vol. 19, No. 3, pp. 218-229, 2014 A NETWORK TRAFFIC PREDICTION MODEL BASED ON QUANTUM INSPIRED PSO AND WAVELET NEURAL NETWORK Kun Zhang Department of Mathematics,
More informationTHE system is controlled by various approaches using the
, 23-25 October, 2013, San Francisco, USA A Study on Using Multiple Sets of Particle Filters to Control an Inverted Pendulum Midori Saito and Ichiro Kobayashi Abstract The dynamic system is controlled
More informationNonlinearControlofpHSystemforChangeOverTitrationCurve
D. SWATI et al., Nonlinear Control of ph System for Change Over Titration Curve, Chem. Biochem. Eng. Q. 19 (4) 341 349 (2005) 341 NonlinearControlofpHSystemforChangeOverTitrationCurve D. Swati, V. S. R.
More informationAnalysis of Four Quadrant Operation of Thruster Motor in an AUV using an Optimized H Infinity Speed Controller
Analysis of Four Quadrant Operation of Thruster Motor in an AUV using an Optimized H Infinity Speed Controller K. Vinida 1 and Mariamma Chacko 2 1 Research Scholar, Department of ship technology, Cochin
More informationReview on Aircraft Gain Scheduling
Review on Aircraft Gain Scheduling Z. Y. Kung * and I. F. Nusyirwan a Department of Aeronautical Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.
More informationCapacitor Placement for Economical Electrical Systems using Ant Colony Search Algorithm
Capacitor Placement for Economical Electrical Systems using Ant Colony Search Algorithm Bharat Solanki Abstract The optimal capacitor placement problem involves determination of the location, number, type
More informationAdaptive Dual Control
Adaptive Dual Control Björn Wittenmark Department of Automatic Control, Lund Institute of Technology Box 118, S-221 00 Lund, Sweden email: bjorn@control.lth.se Keywords: Dual control, stochastic control,
More informationAcceleration of Levenberg-Marquardt method training of chaotic systems fuzzy modeling
ISSN 746-7233, England, UK World Journal of Modelling and Simulation Vol. 3 (2007) No. 4, pp. 289-298 Acceleration of Levenberg-Marquardt method training of chaotic systems fuzzy modeling Yuhui Wang, Qingxian
More informationDifferential Evolution Based Particle Swarm Optimization
Differential Evolution Based Particle Swarm Optimization Mahamed G.H. Omran Department of Computer Science Gulf University of Science and Technology Kuwait mjomran@gmail.com Andries P. Engelbrecht Department
More informationHybrid PSO-ANN Application for Improved Accuracy of Short Term Load Forecasting
Hybrid PSO-ANN Application for Improved Accuracy of Short Term Load Forecasting A. G. ABDULLAH, G. M. SURANEGARA, D.L. HAKIM Electrical Engineering Education Department Indonesia University of Education
More informationWIND SPEED ESTIMATION IN SAUDI ARABIA USING THE PARTICLE SWARM OPTIMIZATION (PSO)
WIND SPEED ESTIMATION IN SAUDI ARABIA USING THE PARTICLE SWARM OPTIMIZATION (PSO) Mohamed Ahmed Mohandes Shafique Rehman King Fahd University of Petroleum & Minerals Saeed Badran Electrical Engineering
More informationPID control of FOPDT plants with dominant dead time based on the modulus optimum criterion
Archives of Control Sciences Volume 6LXII, 016 No. 1, pages 5 17 PID control of FOPDT plants with dominant dead time based on the modulus optimum criterion JAN CVEJN The modulus optimum MO criterion can
More informationShort-term Wind Prediction Using an Ensemble of Particle Swarm Optimised FIR Filters
Short-term Wind Prediction Using an Ensemble of Particle Swarm Optimised FIR Filters J Dowell, S Weiss Wind Energy Systems Centre for Doctoral Training, University of Strathclyde, Glasgow Department of
More informationTracking Control of an Ultrasonic Linear Motor Actuated Stage Using a Sliding-mode Controller with Friction Compensation
Vol. 3, No., pp. 3-39() http://dx.doi.org/.693/smartsci.. Tracking Control of an Ultrasonic Linear Motor Actuated Stage Using a Sliding-mode Controller with Friction Compensation Chih-Jer Lin,*, Ming-Jia
More informationAvailable online at ScienceDirect. Procedia Computer Science 20 (2013 ) 90 95
Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 20 (2013 ) 90 95 Complex Adaptive Systems, Publication 3 Cihan H. Dagli, Editor in Chief Conference Organized by Missouri
More informationFinding Robust Solutions to Dynamic Optimization Problems
Finding Robust Solutions to Dynamic Optimization Problems Haobo Fu 1, Bernhard Sendhoff, Ke Tang 3, and Xin Yao 1 1 CERCIA, School of Computer Science, University of Birmingham, UK Honda Research Institute
More informationReactive Power and Voltage Control of Power Systems Using Modified PSO
J. Energy Power Sources Vol. 2, No. 5, 2015, pp. 182-188 Received: March 29, 2015, Published: May 30, 2015 Journal of Energy and Power Sources www.ethanpublishing.com Reactive Power and Voltage Control
More informationNON-LINEAR CONTROL OF OUTPUT PROBABILITY DENSITY FUNCTION FOR LINEAR ARMAX SYSTEMS
Control 4, University of Bath, UK, September 4 ID-83 NON-LINEAR CONTROL OF OUTPUT PROBABILITY DENSITY FUNCTION FOR LINEAR ARMAX SYSTEMS H. Yue, H. Wang Control Systems Centre, University of Manchester
More informationImproving on the Kalman Swarm
Improving on the Kalman Swarm Extracting Its Essential Characteristics Christopher K. Monson and Kevin D. Seppi Brigham Young University, Provo UT 84602, USA {c,kseppi}@cs.byu.edu Abstract. The Kalman
More informationIterative Controller Tuning Using Bode s Integrals
Iterative Controller Tuning Using Bode s Integrals A. Karimi, D. Garcia and R. Longchamp Laboratoire d automatique, École Polytechnique Fédérale de Lausanne (EPFL), 05 Lausanne, Switzerland. email: alireza.karimi@epfl.ch
More informationInternational Journal of Scientific & Engineering Research, Volume 7, Issue 8, August-2016 ISSN
ISSN 2229-5518 374 Designing fractional orderpid for car suspension systems using PSO Algorithm Saeed Zeraati Department of Electrical Engineering,Islamic Azad university,gonabad Branch,Gonabad,Iran Abstract
More informationGaussian Process for Internal Model Control
Gaussian Process for Internal Model Control Gregor Gregorčič and Gordon Lightbody Department of Electrical Engineering University College Cork IRELAND E mail: gregorg@rennesuccie Abstract To improve transparency
More informationPARTICLE swarm optimization (PSO) is one powerful and. A Competitive Swarm Optimizer for Large Scale Optimization
IEEE TRANSACTIONS ON CYBERNETICS, VOL. XX, NO. X, XXXX XXXX 1 A Competitive Swarm Optimizer for Large Scale Optimization Ran Cheng and Yaochu Jin, Senior Member, IEEE Abstract In this paper, a novel competitive
More informationA Boiler-Turbine System Control Using A Fuzzy Auto-Regressive Moving Average (FARMA) Model
142 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 18, NO. 1, MARCH 2003 A Boiler-Turbine System Control Using A Fuzzy Auto-Regressive Moving Average (FARMA) Model Un-Chul Moon and Kwang Y. Lee, Fellow,
More informationAN INTELLIGENT HYBRID FUZZY PID CONTROLLER
AN INTELLIGENT CONTROLLER Isin Erenoglu Ibrahim Eksin Engin Yesil Mujde Guzelkaya Istanbul Technical University, Faculty of Electrical and Electronics Engineering, Control Engineering Department, Maslak,
More informationThe Design of Sliding Mode Controller with Perturbation Estimator Using Observer-Based Fuzzy Adaptive Network
ransactions on Control, utomation and Systems Engineering Vol. 3, No. 2, June, 2001 117 he Design of Sliding Mode Controller with Perturbation Estimator Using Observer-Based Fuzzy daptive Network Min-Kyu
More informationHover Control for Helicopter Using Neural Network-Based Model Reference Adaptive Controller
Vol.13 No.1, 217 مجلد 13 العدد 217 1 Hover Control for Helicopter Using Neural Network-Based Model Reference Adaptive Controller Abdul-Basset A. Al-Hussein Electrical Engineering Department Basrah University
More informationA Discrete Robust Adaptive Iterative Learning Control for a Class of Nonlinear Systems with Unknown Control Direction
Proceedings of the International MultiConference of Engineers and Computer Scientists 16 Vol I, IMECS 16, March 16-18, 16, Hong Kong A Discrete Robust Adaptive Iterative Learning Control for a Class of
More informationTraffic Signal Control with Swarm Intelligence
009 Fifth International Conference on Natural Computation Traffic Signal Control with Swarm Intelligence David Renfrew, Xiao-Hua Yu Department of Electrical Engineering, California Polytechnic State University
More informationMULTILOOP PI CONTROLLER FOR ACHIEVING SIMULTANEOUS TIME AND FREQUENCY DOMAIN SPECIFICATIONS
Journal of Engineering Science and Technology Vol. 1, No. 8 (215) 113-1115 School of Engineering, Taylor s University MULTILOOP PI CONTROLLER FOR ACHIEVING SIMULTANEOUS TIME AND FREQUENCY DOMAIN SPECIFICATIONS
More informationSensor Localization and Target Estimation in Visual Sensor Networks
Annual Schedule of my Research Sensor Localization and Target Estimation in Visual Sensor Networks Survey and Problem Settings Presented in the FL seminar on May th First Trial and Evaluation of Proposed
More informationQUICK AND PRECISE POSITION CONTROL OF ULTRASONIC MOTORS USING ADAPTIVE CONTROLLER WITH DEAD ZONE COMPENSATION
Journal of ELECTRICAL ENGINEERING, VOL. 53, NO. 7-8, 22, 197 21 QUICK AND PRECISE POSITION CONTROL OF ULTRASONIC MOTORS USING ADAPTIVE CONTROLLER WITH DEAD ZONE COMPENSATION Li Huafeng Gu Chenglin A position
More informationSimulation based Modeling and Implementation of Adaptive Control Technique for Non Linear Process Tank
Simulation based Modeling and Implementation of Adaptive Control Technique for Non Linear Process Tank P.Aravind PG Scholar, Department of Control and Instrumentation Engineering, JJ College of Engineering
More informationNDI-BASED STRUCTURED LPV CONTROL A PROMISING APPROACH FOR AERIAL ROBOTICS
NDI-BASED STRUCTURED LPV CONTROL A PROMISING APPROACH FOR AERIAL ROBOTICS J-M. Biannic AERIAL ROBOTICS WORKSHOP OCTOBER 2014 CONTENT 1 Introduction 2 Proposed LPV design methodology 3 Applications to Aerospace
More information