INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET)

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1 INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 ISSN (Print) ISSN (Online) Volume 3, Issue 2, July-December (2012), pp IAEME: Journal Impact Factor (2012): (Calculated by GISI) IJARET I A E M E STUDY OF MODEL PREDICTIVE CONTROL USING NI LabVIEW Dr.V.BALAJI Principal cum Professor, Department of Electrical and Electronics Engineering Lord Ayyappa Institute of Engineering and Technology, Kanchipuram. India Id: balajieee79@gmail.com ABSTRACT This paper introduces the application of virtual instruments implemented using the national instruments LabView software with various objectives in control system engineering education. The main of this paper is to provide a better understanding in the performance of model predictive control (MPC). This current paper discuses how to create a MPC for a simple model, MPC simple model with time delay and MPC versus PID controller. The scope of this paper is to give an overview of the MPC implementation in LabVIEW. The simulated results clearly explain the performance of the MPC and the difference between MPC and PID controller. Keywords: Control systems, Graphical Programming, Model Predictive Control (MPC), NI LabVIEW, PID controller, Simulation, Software I INTRODUCTION Now a day s control systems engineers in the industry are using computer aided control systems design for modeling, system identification and estimation. These make a way to study graphical programming software tools and also becoming indispensable for teaching control systems theory and its applications. By adopting simulations the students may easily visualize the effect of adjusting different parameters of a system and the overall performance of the system can be viewed. Moreover it would be a ideal if such tools are not only utilized in relevant industries but it also be taught in the classroom.ni Labview has proven to be an invaluable tool in decreasing development time in research, design, validation, production and manufacturing cost. The major advantages of labview include ease of learning, debugging, and simplicity of using interface, reliable performance and capability of controlling the equipment. In this paper it is demonstrated how to create a model predictive control for a first order system, first order system with time delay in a Lab VIEW environment and also explains virtually the difference between MPC and PID controller. The simulations are conducted using control design simulation tool box in a graphical programming environment. Section 2 of this research paper is brief introduction of Model Predictive control. Section 3 is about the introduction of NI Labview. Section 4 deals with implementation of MPC in Lab VIEW. 257

2 Section 5 describes the simulation results of MPC. Section 6 is the conclusion of this research paper. Section 7 contains the reference used in this paper. I I INTRODUCTION TO MPC Model Predictive Control, or MPC, is an advanced method of process control that has been in use in the process industries such as chemical plants and oil refineries. Model predictive controllers rely on dynamic models of the process, most often linear empirical models obtained by system identification. Model predictive control (MPC) refers to a class of computer control algorithms that utilize an explicit process model to predict the future response of a plant. At each control interval an MPC algorithm attempts to optimize future plant behavior by computing a sequence of future manipulated variable adjustments. The first input in the optimal sequence is then sent into the plant, and the entire calculation is repeated at subsequent control intervals. Model predictive control (MPC) is a technique that focuses on constructing controllers that can adjust the control action before a change in the output set point actually occurs. This predictive ability, when combined with traditional feedback operation, enables a controller to make adjustments that are smoother and closer to the optimal control action values. MPC consists of an optimization problem at each time instants, k. The main point of this optimization problem is to compute a new control input vector to be feed to the system, and at the same time take process constraints into considerations. An MPC algorithm consists of a Cost function, Constraints, Model of the process. I II INTRODUCTION TO NI LABVIEW SOFTWARE LabVIEW StandS for Laboratory Virtual Instrumentation Engineering Workbench. The Labview environment consists of two programming layers a front panel and a block diagram.the front panel is built with controls and indicators, which are the interactive input and output terminals of the VI respectively. LabVIEW has many built in functions such as I/O data communication, state charts, Mathematics, Signal Processing, System Identification and Estimation. Control Design Simulation Module. Using above mentioned functions of LabVIEW MPC Model was simulated. IV 4.1Model Construction CONTROL DESIGN AND SIMULATION USING LABVIEW The Control Design and Simulation and predictive control palette in LabVIEW is shown in figure 1 and 2 respectively. Figure 1 The Control Design Palette in LabVIEW 258

3 Figure 2 The Predictive Control Palette in LabVIEW The Model Construction Palette is shown in figure 3 and also shows how many types models is available in the control design and simulation module. Figure 3 The Model Construction Palette The VIs in this section is used to construct various types of Models like State Space, Transfer Function, and Zero-Pole-Gain. The Construct State Space Model and Construct Transfer Function Model functions are shown in figure 4 and 5 respectively. We use the CD Create MPC Controller VI to create an MPC Controller. The MPC created on a state-space model. The CD implement MPC Controller is used to calculate the control values for each sampling time and it is implemented in a While Loop. 259

4 4.2 CD Construct State Space Model Figure 4 CD Construct State Space Model.VI The terminals for the function are shown above. If the Sampling Time terminal is not connected, the system is by default considered continuous. Connecting a value to Sampling Time will change the system to discrete time using the given sampling time. There are terminals for the A, B, C, and D matrices of the State Space model. Once LabVIEW creates the State-Space model (available at the output terminal), it can be used for other functions and can be converted into other forms. 4.3 CD Construct Transfer Function Model Figure 5 CD Transfer Function Model.VI The terminals are shown above. The important terminals are the Numerator and Denominator. As in the previous case, once the model is created, it can either be displayed on the Front Panel or connected to other functions. 4.4 CONSTRUCTION OF PID ACADEMIC CONTROLLER The VI shown below shows how to create and display an PID Academic controller.ie standard parallel PID controller. Figure 6 Block Diagram of PID Academic 260

5 V SIMULATION OF MPC IN LabVIEW 5.1 First order Model In this section we will consider a first model using LabVIEW Consider a first order system given below. Where T is the time constant for the system K is the pump gain We set T = 8s and K = 4 Substitute the values in the above equation we get The front Panel diagram with a wave form a simple model is shown in the figure 7.. Figure 7 Front panel Diagram for a Simple Model From the wave form we clearly understand the Performance of MPC how it moves to reach the set point. 261

6 Constraints and Weighting 5.2 Model with Delay Time We consider the following system X = - 1/T + Ku (t- ) We set the values as T = 8s and K = 4 and = 4 Where = time delay The MPC algorithm requires that the model is a linear state-space model, but the time delay causes problems. A solution could be to transform the differential equation we have to a transfer function. Then we can use built-in functions in LabVIEW to convert it to a linear state-space model. Applying LT to the above equation we get H(s) = x(s)/u(s) = Substitute the values as T = 8s and K = 4 and = 4 We get the final expression H(s) = x(s)/u(s) = The figure 8 shows the front panel diagram of a simple model with a time delay and also it shows how MPC reaches the set point with a time delay of 4 s. 262

7 Figure 8 Front panel Diagram of a Simple Model with a Time Delay Figure 9 MPC Parameters 5.3 MPC VS PID Controller Figure 9a Front Panel Diagram of MPC Controller 263

8 Figure 10 Front Panel Diagram of PID Controller Figure 11 Block Diagram of MPC Controller 264

9 Figure 12 Block Diagram of PID Controller From the waveforms of figure 9and 10 we see the main difference between a MPC controller and a more traditional PID controller. Another main difference between MPC and PID is that MPC can handle MIMO (Multiple Inputs, Multiple Outputs) systems, while PID is used for SISO systems (Single Input, Single Output). From the figure 9 & 10 we can analyze the difference MPC and PID Controller. The difference between them is summarized below S.No MPC Controller PID Controller 1. Constraints included in the design No knowledge about constraints A mathematical model is not needed Improved process operation A mathematical model is not needed A mathematical model is not needed A mathematical model is not needed Not optimal process operation A mathematical model is not needed A mathematical model is not needed Table 1 Difference between MPC and PID 265

10 VI CONCLUSION The idea of Model Predictive Control Simulation using NI LabVIEW is being put into operation successfully. This paper clearly explained the depth of MPC implemented through LabVIEW. The results illustrate the performance of MPC and also clearly state the difference between the PID and MPC controller. These simulation results are useful to do the required modifications in control system industry for optimal control. VII REFERNCES [1] Erik Luther (2012), Introduction to Control Design and Simulation using LabVIEW Rice University, Houston, Texas. [2] Camacho E. F and Bordons C. (1999) Model Predictive Control, Springer, London. [3] Maciejowski J. M. (2002) Predictive Control with Constraint, Prentice Hall. [4] National Instruments, LabVIEW User Manual at [5] by Finn Haugen. [6] based on Prof. Dawn Tilbury stutorials from University of Michigan. ABOUT THE AUTHOR Dr.V.BALAJI has 12 years of teaching experience. Now he is working as a principal in Lord Ayyappa Institute of Engineering and Technology, Kanchipuram. His current areas of research are model predictive control, process control, and Fuzzy and Neural Networks. He has published 26 research papers in national and international journals and conferences. He is a member of ISTE, IEEE, IAENG, IAOE and IACSIT. 266