DESIGN, IMPLEMENTATION, AND REAL- TIME SIMULATION OF A CONTROLLER- BASED DECOUPLED CSTR MIMO CLOSED LOOP SYSTEM

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1 International Journal of Electrical Engineering & Technology (IJEET) Volume 7, Issue 3, May June, 2016, pp , Article ID: IJEET_07_03_011 Available online at ISSN Print: and ISSN Online: Journal Impact Factor (2016): (Calculated by GISI) IAEME Publication DESIGN, IMPLEMENTATION, AND REAL- TIME SIMULATION OF A CONTROLLER- BASED DECOUPLED CSTR MIMO CLOSED LOOP SYSTEM Julius Ngonga Muga, Raynitchka Tzoneva and Senthil Krishnamurthy Cape Peninsula University of Technology Department of Electrical, Electronic and Computer Engineering Bellville Campus, P.O. Box 1906, Bellville, South Africa ABSTRACT In this paper, dynamic decoupling control design strategies for the MIMO Continuous Stirred Tank Reactor (CSTR) process characterised by nonlinearities, loop interaction and the potentially unstable dynamics, are presented. Simulations of the behavior of the closed loop decoupled system are performed in Matlab/Simulink. Software transformation technique is proposed to build a real-time module of the developed in Matlab/Simulink environment software modules and to transfer it to the real-time environment of TwinCAT 3.1 software of the Beckhoff PLC. The simulation results from the investigations done in Simulink and TwinCAT 3.1 software platforms have shown the suitability and the potentials of the method for design of the decoupling controller and of merging the Matlab/Simulink control function blocks into the TwinCAT 3.1 function blocks in real-time. The merits derived from such integration imply that the existing software and its components can be re-used. The paper contributes to implementation of the industrial requirements for portability and interoperability of the PLC software. Key words: Continuous Stirred Tank Reactor, Decoupling control, Closed loop system, Programmable Logic Controller, Real-time simulation Cite this Article: Julius Ngonga Muga, Raynitchka Tzoneva and Senthil Krishnamurthy, Design, Implementation, and Real-Time Simulation of A Controller-Based Decoupled CSTR MIMO Closed Loop System. International Journal of Electrical Engineering & Technology, 7(3), 2016, pp editor@iaeme.com

2 Design, Implementation, and Real-Time Simulation of A Controller-Based Decoupled CSTR MIMO Closed Loop System 1. INTRODUCTION The control of the MIMO Continuous Stirred Tank Reactor (CSTR) process requires a careful design because of its existing nonlinearities, loop interactions and the potentially unstable dynamics. Various methods for design of controllers for this process are based on utilisation of the linear and nonlinear control theories. Thus developing and implementing controllers which are suitable when process nonlinearities must be accounted for, is of great interest for both academy and industry. Plenty of research papers on the analysis and control of nonlinear systems are available and many different methods have been proposed. Such approaches are feedback linearization, back stepping control, sliding mode control, trajectory linearization based on Lyapunov theory, those based on Differential Geometry concepts, as well as those based on artificial computing approaches, etc. A few examples are from [18], [19], [20], [21], and [22]. Another challenging aspect is if the system to be controlled is Multi-Input Multi- Output (MIMO). In MIMO systems the coupling between different inputs and outputs makes the controller design to be difficult. Generally, each input will affect every output of the system. Because of this coupling, signals can interact in unexpected ways. One solution is to design additional controllers to compensate for the process and control loop interactions [23], [24], and [25]. The method, investigated in the paper for design of a controller for the CSTR is based on linearisation and decoupling of the linearised process model into independed SISO submodels. Decoupling control pre-compensates for the interactions so that each output is controlled independently. This control strategy has been used by several other authors over the years with success, among them [6], [8] and [10]. Another problem in industry is that the existing PLCs have only linear PID controllers to be used and it is difficult to program more complex linear or nonlinear controllers in their software environment. New approach to solve this problem is to transform the models of controllers and control systems build in Matlab/Simulink to models capable to be used for real-time implementation in a PLC. The paper presents a methodology for transforming the developed continuous time controller blocks as well as the complete closed loop systems from Matlab/Simulink environment to the Beckhoff PLC automation software using the capabilities of TwinCAT 3.1 simulation environment for real-time control. The Beckhoff CX5020 Programmable Logic Controller [5] is used for the closed loop real-time control system simulation to show the effectiveness of the control laws developed for dynamic decoupling control. The rest of the paper is structured as follows: In section 2, Mathematical modeling of the nonlinear MIMO CSTR in the Matlab/Simulink platform is presented. In section 3, the design of the dynamic decoupling controller for the MIMO CSTR process is described. Section 4 presents the design of the decentralized control for the MIMO CSTR process. Section 5 describes the transformation procedure of the developed software from the Matlab/Simulink environment to Beckhoff TwinCAT 3 real-time environment and the results of the real-time simulation. Section 6 gives the conclusion of the paper. 2. THE IDEAL CSTR PROCESS The Continuous Stirred Tank Reactor (CSTR) process model is used as a case study in the design and implementation of various control laws, due to the simplicity o f the mathematical representation and because of the inherent nonlinearity property of the editor@iaeme.com

3 Julius Ngonga Muga, Raynitchka Tzoneva and Senthil Krishnamurthy model. An exothermic CSTR is a common phenomenon in chemical and petrochemical reaction plants in which an impeller continuously stirs the content of a tank or reactor, thereby ensuring proper mixing of the reagents in order to achieve a specific output (product). The process is normally run at steady state with continuous flow of reactants and products. Exothermic reactors are the most interesting systems to study because of the potential safety problems (rapid increase in temperature behavior) and possibility of the exotic behavior such as multiple steady states. This means that for the same value of the input variable there may be several possible values of the output variable [1], [2], [4], [9], [14], [15] and [17]. These features therefore make the CSTR an important model for research. Although industrial reactors typically have more complicated kinetics than an ideal CSTR, the characteristic behavior is similar; hence the interesting features can still be realized using the ideal one. In addition, the CSTR is an example of a MIMO system in which the formation of the product is dependent upon the reactor temperature and the feed flow rate. The process has to be controlled by two loops, a concentration control loop and a temperature control loop. Changes to the feed flow rate are used to control the product concentration and the changes to the reactor temperature are made by increasing or decreasing the temperature of the jacket (varying the coolant flow rate). However, changes made to the feed would change the reaction mass, and hence temperature, and changes made to temperature would change the reaction rate, and hence influence the concentration. This is therefore an example of loop interaction process. For control design, loop interactions should be avoided because changes in one loop might cause destabilizing effects on the other loop. The basic scheme of the CSTR process is shown in Figure 1. q in Stirrer Fresh Feed of A C AO T O q c T CO T C A Coolant jacket q c TCO Inlet coolant temperature Effluent T C A q Figure 1 A basic scheme of the CSTR Process Dynamic behavior of the considered CSTR process is developed using mass, component and energy balance equations [7], [13]. For this study, the system is assumed to have two state variables; the reactor temperature and the reactor concentration and these are also the output variables to be controlled. The manipulated variables are the feed flow rate and the coolant flow rate. The system is modelled and analyzed using the parameters specified in Tables 1 and Table 2. These parameters represent both the steady state and the dynamic operating conditions [16], [17]. The process has three steady state operating points, given in Table 2. The model is given by: editor@iaeme.com

4 Design, Implementation, and Real-Time Simulation of A Controller-Based Decoupled CSTR MIMO Closed Loop System ( ( ), ( )) dca ( ) q f C t T t t ( C ( t ) C ( t )) r ( t ) 1 A AO A dt V (1) dt q ( H) r C q ha c PC c f2( CA, T) 0 ( To T) 1exp TCO T dt V CP CPV CPqc (2) The nonlinearity of the model is hidden mainly in the computation of the reaction rate, r which is a nonlinear function of the temperature T and it is computed from the Arrhenius law, as follows: E r k exp( ) C o A (3) RT CA is the measured product concentration, A0 where C is the feed concentration, k0 is the reaction rate constant or the pre-exponential factor, T0 is the feed temperature, TCO is the Inlet coolant temperature, T is the measured reactor temperature, qc is the coolant flow rate, q is the process feed flow rate, and c are the liquid densities, CP and C PC are the specific heat capacities of the liquids, R is the universal gas constant, E is the activation energy, ha is the heat transfer term H is the heat of the reaction and V is the CSTR volume. Process variable Table 1 Steady state operating data Nominal operation Process variable condition mol / CSTR volume ) Nominal operation condition Reactor Concentration ( C A) l (V 100 l Temperature (T ) K Heat transfer term (ha) 7*10 cal /(min. k) Coolant flow rate ( q c ) 103l / min Reaction rate constant ( o ) k 1 7.2*10 10 min Process flow rate (q) 100.0l / min Activation energy ( E / R) 1 *10 4 K Feed concentration ( C AO) 1 mol / l Heat of reaction ( H ) 5 2*10 cal / mol Feed temperature ( T O ) 350.0K Liquid densities (, c ) 3 1*10 gal / l Coolant temperature ( T CO ) 350.0K Specific heats ( C P, CPC ) 1cal /( g. k) Table 2 Steady state operating points Operating points q (lpm) q C (lpm) C A ( mol / l) T (K) Operating points Operating points Operating points For the process dynamic analysis, the steady state values from Table 2 for the operating point 1 are taken as the initial conditions. The process was simulated for ( 10%) step changes in each input variable in the Matlab environment. One of the input variables was kept at the nominal value and the other was changed. The results are shown in Figures 2 a and 2b. The simulation results demonstrate that the CSTR process exhibits highly nonlinear dynamic behaviour because of the coupling and the inter-relationships of the states, and in particular, the exponential dependence of each state on the reactor temperature as well as the reaction rate being an exponential editor@iaeme.com

5 Temperature response of A [K] Concentration CA of A [mol/l] Julius Ngonga Muga, Raynitchka Tzoneva and Senthil Krishnamurthy function of the temperature. This type of nonlinearity is generally considered significant [17] X: Y: open loop step response curves for concentration X: Y: X: Y: X: Y: % step change in q with qc constant X: Y: % step change in q with qc constant nominal values of q and qc X: Y: % step change in q with qc constant X: Y: % step change in qc with q constant 0.05 Time [minutes] a) open loop step response curves X: Y: X: Y: X: Y: % step change in qc with q constant +10% step change in X: q with qc constant Y: response for nominal values of q and qc X: Y: X: Y: % step change in q with qc constant X: Y: % step change in qc with q constant 434 Time [minutes] b) Figure 2 Time response of a) concentration and b) temperature for (±10%) step changes in q and q c Hence, there rises a need to develop control schemes that are able to achieve tighter control of the process dynamics. Decoupling control strategy is investigated in the paper to evaluate its capabilities to control the CSTR process. It requires that the nonlinear system be linearized at the given operating point and the resulting state space equations can then be directly used in the design of standard linear controllers. 3. DECOUPLING CONTROL STRATEGY 3.1. Linearization and stability analysis The linearization method is applied to the nonlinear CSTR model of equations (1)- (3) to give a state space representation where, the state, input, and output vectors are in the deviation variable form and defined by the following: editor@iaeme.com

6 Design, Implementation, and Real-Time Simulation of A Controller-Based Decoupled CSTR MIMO Closed Loop System C - C x A AS 1 x = = = State variables T -T x S 2, q - q u q - q u c cs 2 s 1 u = = = Control variables C - C x T -T x S 2 A AS 1 y = = = Output variables where CAS, TS, qs and q cs are the steady state values of the effluent concentration, reactor temperature, the feed flow rate, and the coolant flow rate respectively.using 4 the values of the parameters provided in Tables 1 and 2, and letting, a = E / R 1*10, (-H)k a = 2 rc p o rc 13 c pc -ha 1.44*10, a = 0.01, 3 rc V and a= 4 700, the Equations (1) - (3) may rc p p be written as: -a 1/ x (C 2 AO - x 1 )u1 f 1(x 1,x 2 )= -kox1e + V (4) 1 -a 1/ x (T 2 O - x 2 )u1 -a 4 / u2 f 2(x 1,x 2 )= a2x1e + +a 3(TCO - x 2 )u 2*(1- e ) V (5) Then state space equation matrices for the CSTR model (4) and (5) are derived from the corresponding Jacobian matrices in terms of x and u from which the matrices of the linear model of the process are: u1 ko ( a1k ox1 ) ( a1 / x2 ) 2 ( a1 / x2 ) V e ( x2 e ) A a2 1 u1 ( a1a 2x1) au ( 3 2( 1) a1 / a2 ) ( a4 / u2 ) 2 ( a1 / x2 ) e e V ( x2 e ) ( CAo x1 ) 0 V B ( To x2) 1 ( a3a4 ( TCO x2)) a3( 1)( T ( 2) a4/ u2) CO x V e ( u2 exp( a4 / u2)) Substitution of the nominal steady state parameter values at the given operating point 1 in the above matrices, it is obtained: A B C (6) From Equation (6) the matrix transfer function of the linearized CSTR is found to be: s Ys () G ( s) G ( s) s 5.99s s 5.99s G () s P Us () G ( s) G ( s) s s s 5.99s s 5.99s editor@iaeme.com

7 Julius Ngonga Muga, Raynitchka Tzoneva and Senthil Krishnamurthy U1( s) Y1( s) U( s) ; Y( s) U2( s) Y2( s) where, (7) As can be seen from the transfer function formed, the inputs and outputs are interacting. Thus, a disturbance at any of the inputs causes a response in all the two outputs. Such interactions make control and stability analysis very complicated. Consequently, it is not immediately clear which input to use to control the individual outputs. It is therefore necessary to reduce or eliminate the interactions by designing control system that compensates for such interactions so that each output can be controlled independently of the other output Decoupling controller design A systematic design procedure is presented for the case of a dynamic decoupling strategy for the system under study. The control objective is to control Y 1(s) and Y 2(s) independently, in spite of changes in U 1 (s) and U 2 (s). Therefore, to meet these objectives, the first step is to design the decouplers and secondly, to design the controllers for the decoupled systems. Most decoupling approaches use the scheme depicted in Figure 3 where the apparent plant model is diagonal. Apparent plant model R1( s) E () s 1 V () s 1 U () s 1 Y() s 1 R () s 2 E () s 2 Controller Decoupler U () s Pant model 2 Cs () V () s 2 Ds () GP () s Y () s 2 Figure 3 The decoupled closed loop control system Decoupling at the input of a 22 x process transfer function G (s) P requires the design of a transfer function matrix D(s), such that matrix Q(s), where; G (s)d(s) P is a diagonal transfer function D ( s) D ( s) G ( s) G ( s) D( s), G ( s) P D ( s) D ( s) G ( s) G ( s) Q ( s) 0 11 Y1( s) V1( s) Qs () and Q() s 0 Q ( s) Q( s) GP ( s) D( s) Y 22, 2( s) V2( s) (8) For complete decoupling the decouplers should be designed according to the equation: 1 D( s) G ( s). Q( s) P (9) Then the diagonal elements of the decoupler are set to be 1 and the off-diagonal elements are as follows: editor@iaeme.com

8 Design, Implementation, and Real-Time Simulation of A Controller-Based Decoupled CSTR MIMO Closed Loop System 1 D( s) 1 Ds () D ( s) 1 2 (10) ( ) ( ) D ( s) G s ; D ( s) G s 1 2, G ( s) G ( s) G Qs () G ( s) G ( s) G () s 22 G ( s) G ( s) 0 G ( s) ( s) G () s 11 (11) This choice makes the realization of the decoupler easy. It ensures two independent SISO control loops. However the diagonal transfer matrix Qs () becomes complicated. This may require an approximation of each term in equation (11) by a simpler transfer function in order to facilitate easier controller Cs () tuning. In this work, simpler approximations are made possible by representing Qs () in the zero/pole/gain form of first order and then designing additional controllers based on these approximations. Thus, in the presence of the decouplers, the TITO process is presented as two independent SISO first order transfer functions, as follows: G* 11 ( s) ( s 13.93), G *( s) ( s 2.85) (12) 3.3. PI-controller design Two independent PI controllers are designed for each apparent loop using a pole placement technique. The relationship between the location of the closed loop poles and the various time-domain specifications of the process transition behavior are considered. The design objective is to maintain the system outputs close to the desired values by driving the output errors to zero at steady state with minimum settling times. To have no steady state error a controller must have integral action. The decoupled closed loop system is given in Figure 4. R () s 1 E () s 1 C1 U () s 11 D2 U () s 12 U () s 1 G11 G12 Y () s 11 Y () s 12 Y() s 1 D1 U () s 21 G21 Y () s 21 R () s 2 E () s 2 C2 U () s 22 U () s 2 G22 Y () s 22 Y () s 2 Figure 4 The decoupled closed loop system editor@iaeme.com

9 Julius Ngonga Muga, Raynitchka Tzoneva and Senthil Krishnamurthy It is therefore prudent to use the PI controller which has the ideal transfer function of the form: C(s)= K P(1+K I(1 / s)), where K P and KIare the controller tuning parameters representing its gain constant and the integral gain constant. The pole placement design method attempts to find a controller setting that gives desired closed loop poles. Thus the controller transfer function matrix Cs () of the system under consideration is given by: Cs () C( s) 0 K 1 (1 K ) 0 P1 I1 1 s 0 C2( s) 1 0 P2(1 I2 ) K K s (13) The outputs of the two separate non-interacting closed loops are: ( K s K K ) Y ( s) R ( s) P1 P1 I s s( KP 1) KP 1KI ( K s K K ) Y ( s) R ( s) P2 P2 I s s( KP2) ( KP2KI 2) (15) The denominators of the above transfer functions are used in a developed pole placement procedure to determine the values of the parameters of the two PI controllers. 4. MATLAB/SIMULINK SIMULATION Simulation results are used to verify the performance of the closed loop system. The Simulink block diagram is given in Figure 5. Two types of investigations are done for every control loop: 1) Changing the values of the set points and 2) Changing the values of the set points under noise conditions in the input and output of the corresponding closed loop and in the control input and output of the other control loop. (14) Figure 5 Dynamic decoupling control implemented in Simulink editor@iaeme.com

10 Temperature[K] Concentration[mol/L] Design, Implementation, and Real-Time Simulation of A Controller-Based Decoupled CSTR MIMO Closed Loop System The time response characteristics of the closed loop TITO CSTR processes for the concentration and temperature are illustrated in Figures 6a and 6b respectively: Closed loop response of the nonlinear CSTR process under the dynamic decoupling control X: 1.26 Y: ysp1= ysp2=0.13 ysp3=0.1 Mp=10.2% ts=0.311min tr=0.127min Setpoint Tracking concentration response Time [min] Closed loop response of the nonlinear CSTR process under the dynamic decoupling control 460 a) 455 X: 0.62 Y: decouling tracking temperature response Setpoint 440 Time [min] b) Figure 6 Set-point tracking a) concentration response and b) temperature response Several other variations in the set-point are investigated to evaluate the time response performance indices for the rising time, settling time, peak overshoot, and steady state errors. The investigation showed that the indices remain constant throughout the set-point variations, hence the dynamic decoupling control is not sensitive to the set-point variations. Figure 7 and Figure 8 present the closed loop responses under the conditions of noises in the input and output of the same control loop. Figure 9 presents the temperature response when the noises are in the concentration loop input and ouput. Figure 10 presents the concentration response when the noises are in the temperature loop input and output editor@iaeme.com

11 Temperature[K] Concentration[mol/L] Concentration[mol/L] Julius Ngonga Muga, Raynitchka Tzoneva and Senthil Krishnamurthy Closed loop response of the nonlinear CSTR process under the dynamic decoupling control X: 0.76 Y: Setpoint Tracking concentration response with noise on y ysp1= ysp2=0.13 ysp3=0.10 noise of +/-0.04 mol/l 0.04 Time [min] Closed loop response of the nonlinear CSTR process under the dynamic decoupling control 0.14 a) X: 0.68 Y: ysp1= ysp2=0.13 ysp3=0.10 noise on u1 of +/-40L/min Setpoint Tracking concentration response with noise on u1 Time [min] b) Figure 7 Concentration response under noise a) 0.04 mol/lin the ouput and b) 40 l/min in the control input Closed loop response of the nonlinear CSTR process under the dynamic decoupling control X: 0.61 Y: Setpoint Tracking temperature response with noise ysp1=444.7 ysp2=455 noise +/- 8K 435 Time [min] a) editor@iaeme.com

12 Temperature[K] Temperature[K] Design, Implementation, and Real-Time Simulation of A Controller-Based Decoupled CSTR MIMO Closed Loop System Figure 8 Temperature responses under noise a) 8 K in the ouput and b) 40 l/min in the control input Closed loop response of the nonlinear CSTR process under the dynamic decoupling control 460 Setpoint X: 0.63 Y: 459 Tracking temperature response with noise on y1 455 b) ysp1=445 ysp2=455 ysp3=445 noise of +/-0.04 mol/l on y1 440 Time [min] Closed loop response of the nonlinear CSTR process under the dynamic decoupling control 460 Setpoint X: 0.63 Tracking temperature response with noise on u1 Y: a) ysp1=445 ysp2=455 noise on u1 of +/-40l/min 440 Time [min] b) Figure 9 Temperature responses under noise a) 40 l/min in the concentration ouput and b) 0.04 mol/l in the concentration control input editor@iaeme.com

13 Concentration[mol/L] Concentration[mol/L] Julius Ngonga Muga, Raynitchka Tzoneva and Senthil Krishnamurthy Closed loop response of the nonlinear CSTR process under the dynamic decoupling control X: 0.7 Y: Setpoint ysp1= ysp2=0.13 ysp3=0.10 noise on y2 of +/-8K Tracking concentration response with noise on y2 Time [min] Closed loop response of the nonlinear CSTR process under the dynamic decoupling control 0.14 a) X: 0.7 Y: ysp1= ysp2=0.13 ysp3=0.10 noise on u2 of +/-40L/min Setpoint Tracking concentration response with noise on u2 Time [min] b) Figure 10 Concentration responses under noise a) 8 K in the temperature ouput and b) 40 l/min in the temperature control input When the separate controll loops for concentration and temperature are subjected on disturbances in their own inputs and outputs, Figure 7 and 8, good tracking control is still achieved and the designed decoupling system is good at rejecting the random variations. The magnitude of the disturbance is important for smooth set point tracking. Performances of the temperature control loop when the disturbances are in the concentration control loop, and vice versa show that the output of the other output does not influence the considered output, but the input of the other control loop influences the output of the considered one.this implies that there are still some elements of interactions in the system. 5. CLOSED LOOP SYSTEM SIMULATION IN REAL-TIME ENVIRONMENT MATLAB/Simulink simulation of the developed closed loop system for control of the CSTR process has shown good behaviour of the concentration and the temperature under the designed decoupling control. Next question is will this system behave in the same way under real-time conditions. Beckhoff CX5020 Programmable Logic Caontroller (PLC) and its software The Windows Control and Automation Technology (TwinCAT 3.1) through their integration with Matlab/Simulink software allow answer to this question to be given without separately programming in the editor@iaeme.com

14 Design, Implementation, and Real-Time Simulation of A Controller-Based Decoupled CSTR MIMO Closed Loop System environment of TwinCAT 3.1. Special transformation methodology is developed by Beckhoff and Matlab for this purpose. TwinCAT 3.1 is new PC-based PLC automation software that enables control engineers to model and simulate complex, distributed control applications in realtime. In support to interoperability between different platforms, this new software supports the development of control applications in Matlab/Simulink environment and generates executable PLC code based on the models applied to it. To use control programs and controllers designed in Matlab/Simulink with a real PLC after successful tests in simulation, the developed algorithms have to be programmed in real-time capable languages like C++ or PLC code. Matlab/Simulink software is capable of generating codes from the Simulink models to the various targets by using the Embedded Simulink Coder (formerly Real-Time Workshop). With the Simulink Embedded Coder and specially developed supplementary software TE1400 from Beckhoff automation, called the TwinCAT 3.1 Target for Matlab/Simulink, it makes it possible for the generation of C++ code which is then encapsulated in a standard TwinCAT 3.1 module format. This code may be instantiated or loaded into the TwinCAT 3.1 development platform. The TE1400 software acts as an interface for the automatic generation of real-time capable modules, which can be executed on the TwinCAT 3.1 runtime environment. It allows for the generation of the TwinCAT 3.1 runtime modules and provides for the real-time parameter acquisition and visualisation. The real-time capable module is termed the TwinCAT Component Object Model (TcCOM). This module can be imported in the TwinCAT 3.1 environment and contains the input and output of the Simulink model. In this case, the CX5020 PLC acts as a real-time platform for execution of the applications downloaded from the TwinCAT 3.1 development environment through the Ethernet communication platform. Through this connection, real-time communication between the Matlab/Simulink, the TwinCAT 3.1 developed algorithms, and the PLC is provided. Figure 11 shows the transformed Simulink closed loop MIMO CSTR process under dynamic control to the corresponding TwinCAT 3 function blocks (modules). The transformation technique shows that the data and parameter connection are the same in these two platforms and therefore there is a one to one correspondence of function blocks between Simulink and TwinCAT 3.1. Figure 11 Transformed Simulink closed loop model to TwinCAT 3 function blocks 5.1. Experimental results Figures present the behavior of the closed loop system in real-time editor@iaeme.com

15 Julius Ngonga Muga, Raynitchka Tzoneva and Senthil Krishnamurthy a) Figure 12 Concentration response under noise a) 0.04 mol/l in the output and b) 40 l/min in the control input b) a)

16 Design, Implementation, and Real-Time Simulation of A Controller-Based Decoupled CSTR MIMO Closed Loop System Figure 13 Concentration responses under noise a) 8 K in the temperature ouput and b) 40 l/min in the temperature control input b) a) Figure 14 Temperature responses under noise a) 0.04 mol/l in the concentration ouput and b) 40 l/min in the concentration control input Analyses of the obtained figures, further confirm that the designed dynamic decoupling controller settings achieve tracking control of the concentration and temperature set points in real-time situation and validate the performance of the b) editor@iaeme.com

17 Julius Ngonga Muga, Raynitchka Tzoneva and Senthil Krishnamurthy designed controllers. A comparative analysis with the results presented in section 4 for the closed loop system simulation in Matlab/Simulink shows that the overshoot has increased but the other performance indices remain the same This showes that strict requirements for the value of the allowed overshoot have to be followed during the process of design of the process controllers. The influence of the noises over the behavior of both the concentration and the temperature is reduced in the conditions of real-time control. Simulation results verify the suitability of the control for effective set-point tracking control and disturbance effect minimisation in real-time. 6. CONCLUSION In this paper, design and real-time implementation of of MIMO closed loop dynamic decopling control of the CSTR process have been investigated. The simulation results from the investigation done in Simulink and TwinCAT 3 software platforms using the model transformation have shown the suitability and the potentials of merging the Matlab/Simulink control function blocks into the TwinCAT 3.1 function blocks in real-time. The merits derived from such integration implies that the existing software and software components can be re-used. This is in line with the requirements of the industry for portability and interoperability of the PLC programming software environments. Similarly, the simplification of programming applications is greatly achieved. The investigation has also shown that the integration of the Matlab/Simulink models running in the TwinCAT 3.1 PLC do not need any modification, hence confirming that the TwinCAT 3.1 development platform can be used for the design and implementation of controllers from different platforms. ACKNOWLEDGEMENT The authors gratefully acknowledge the authorities of Cape Peninsula University of Technology, South Africa for the facilities offered to carry out this work. The research work is funded by the National Research Foundation (NRF) THRIP grant TP and ESKOM TESP grant for the Center for Substation Automation and Energy Management Systems (CSAEMS) development and growth. REFERENCES [1] Aris, R. and Amundson N. An analysis of chemical reactor stability and control I. Chemical Engineering Science, 7(8), 2000, pp [2] Bakosova, M. and A. Vasickaninova A. Simulation of Robust Stabilization of a Chemical Reactor. ECMS 2009 Proceedings edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel Pedrera, 2009, pp [3] Bansode, P. and Jadhav S. Decoupling based predictive control analysis of a continuous stirred tank reactor. Proc. of the 2015 International Conference on Industrial Instrumentation and Control (ICIC), 2015, [4] Bequette, B. Nonlinear control of chemical processes: a review. Industrial & Engineering Chemistry Research Ind. Eng. Chem. Res., 30, 1990, pp [5] [6] Ghosh, A. and Das S. Decoupled periodic compensation for multi-channel output gain margin improvement of continuous-time MIMO plants. IET Control Theory & Applications IET Control Theory Appl., 6(11), 2012, pp [7] Henson, M. and Seborg D. Input-output linearization of general nonlinear processes. AIChE Journal AIChE J., 36, 1990, pp editor@iaeme.com

18 Design, Implementation, and Real-Time Simulation of A Controller-Based Decoupled CSTR MIMO Closed Loop System [8] Jevtović, B. and Mataušek M. PID controller design of TITO system based on ideal decoupler. Journal of Process Control, 20, 2010, pp [9] Kumar, N. and Khanduja N. Mathematical modeling and simulation of CSTR using MIT rule. Proc of the IEEE 5 th India Intern. Conf. on Power Electronics, [10] Luyben, W. Simple method for tuning SISO controllers in multivariable systems. Industrial & Engineering Chemistry Process Design and Development Ind. Eng. Chem. Proc. Des. Dev., 25, 1986, pp [11] Maghade, D. and Patre B. Decentralized PI/PID controllers based on gain and phase margin specifications for TITO processes. ISA Transactions, 51, 2012, pp [12] [13] Pottman, M. and Seborg D. Identification of non-linear processes using reciprocal multi-quadric functions. Journal of Process Control, 2, 1992, pp [14] Russo, L. and Bequette B. Impact of process design on the multiplicity behavior of a jacketed exothermic CSTR. AIChE Journal AIChE J., 41(1), 1995, pp [15] Uppal, A., Ray, W. and Poore A. On the dynamic behavior of continuous stirred tank reactors. Chemical Engineering Science, 29, 1974, pp [16] Vinodha, R., Abraham, S., and Lincoln, S. Prakash. Multiple Model and Neural based Adaptive Multi-loop Controller for a CSTR Process. International journal of Electrical and Computing engineering. 5(4), 2010, pp [17] Vojtesek, J., Novak, J., and Dostal P. Effect of External Linear Model's Order on Adaptive Control of CSTR. Applied Simulation and Modelling: 2005, pp [18] Desoer, C. and Wang, Y. Foundations of feedback theory for nonlinear dynamical systems. IEEE Trans. Circuits Syst. IEEE Transactions on Circuits and Systems, 27(2): 1980, pp [19] Enqvist, M. and Ljung L. Estimating nonlinear systems in a neighborhood of LTI-approximants. Proceedings of the 41st IEEE Conference on Decision and Control, 2002, pp [20] Schweickhardt, T. and Allgower, F. Linear modelling error and steady-state behaviour of nonlinear dynamical systems. In Proc. 44th IEEE Conf. Decision Control: 2005, pp [21] Marinescu, B. Output feedback pole placement for linear time-varying systems with application to the control of nonlinear systems. Automatica, 46: 2010, pp [22] Hammer, J. State feedback control of nonlinear systems: a simple approach. International Journal of Control, 87(1): 2014, pp [23] Liu, R., Liu, G., and Wu, M. A novel decoupling control method for multivariable systems with disturbances. Proceedings of 2012 UKACC International Conference on Control: 2012, pp [24] Dr. V.Balaji, E.Maheswari, Model Predictive Control Techniques For CSTR Using Matlab. International Journal of Electrical Engineering & Technology, 3(3), 2012, pp [25] Olatunji, O. M. And Ayotamuno, M. J, Simulation of A CSTR Model For Thevetia Peruviana Oil Transesterification In The Production of Biodiesel. International Journal of Electrical Engineering & Technology, 5(7), 2014, pp editor@iaeme.com

19 Julius Ngonga Muga, Raynitchka Tzoneva and Senthil Krishnamurthy [26] Sujatha, V. and Panda, R. Control configuration selection for multi input multi output processes. Journal of Process Control, 23(10): 2013, pp [27] Ghosh, A. & Das, S. Decoupled periodic compensation for multi-channel output gain margin improvement of continuous-time MIMO plants. IET Control Theory & Applications, 6(11): 2012, pp BIOGRAPHIES Julius Ngonga Muga has MTech in Electrical Engineering from the Cape Peninsula University of Technology (CPUT), Cape Town and MSc in Electronic Engineering from the ESIEE, France. He has been a Lecturer at the Technical University of Mombasa, Kenya between 2009 and Since 2013 he has been doing research as a DTech postgraduate at the Department of Electrical, Electronic, and Computer Engineering, CPUT. His research interest is process instrumentation, classic and modern control strategies, industrial automation, and application of soft computing techniques as alternative methods for the control of real-time systems. Raynitchka Tzoneva has MSc. and Ph.D. in Electrical Engineering (control specialization) from the Technical University of Sofia (TUS), Bulgaria. She has been a lecturer at the TUS and an Associate Professor at the Bulgarian Academy of Sciences, Institute of Information Technologies between 1982 and Since 1998, she has been working as a Professor at the Department of Electrical, Electronic, and Computer Engineering, Cape Peninsula University of Technology, Cape Town. Her research interest is in the fields of optimal and robust control design and optimization of linear and nonlinear systems, energy management systems, real-time digital simulations, and parallel computation. Prof. Tzoneva is a Member of the Institute of Electrical and Electronics Engineers (IEEE). Senthil Krishnamurthy received BE and ME in Power System Engineering from Annamalai University, India and Doctorate Technology in Electrical Engineering from Cape Peninsula University of Technology, South Africa. He has been a lecturer at the SJECT, Tanzania and Lord Venkateswara and E.S. College of Engineering, India. Since 2011 he has been working as a Lecturer at the Department of Electrical, Electronic and Computer Engineering, Cape Peninsula University of Technology, South Africa. He is a member of the Niche area Real Time Distributed Systems (RTDS) and of the Centre for Substation Automation and Energy management Systems supported by the South African Research Foundation (NRF). His research interest is in the fields of optimization of linear and nonlinear systems, power systems, energy management systems, parallel computing, computational intelligence and substation automation. He is a member of the Institute of Electrical and Electronic Engineers (IEEE), Institution of Engineers India (IEI), and South African Institution of Electrical Engineers (SAIEE) editor@iaeme.com