INCREMENTAL MODEL PREDICTIVE CONTROL SYSTEM …

1 INCREMENTAL MODEL PREDICTIVE CONTROL SYSTEM design ANDIMPLEMENTATION using matlab /SIMULINKByXIN LINA THESIS PRESENTED TO THE GRADUATE SCHOOLOF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEUNIVERSITY OF FLORIDA2013 2013 Xin Lin2To all chemical engineers3 ACKNOWLEDGMENTSThe author is sincerely thankful to Prof. Oscar D. Crisalle,DistinguishedTeaching Scholar and Professorin the Chemical Engineering Department ofUniversity of Florida, for helpful advice throughout the stage of this work anddoctoral candidates M. Rafe Biswas and Shyam P. Mudiraj for their valuablefeedback on the revisions of this thesis .4 TABLE OF CONTENTS pageACKNOWLEDGEMENTS ..4 LIST OF TABLES ..7 LIST OF FIGURES ..8 ABSTRACT .. 10 CHAPTER1 INTRODUCTION .. Background .. Integral Controller and Offset-free Performance.

2 Overview of MPC .. Description of Contents .. 142 LITERATURE REVIEW .. Discrete Integral Controller .. Standard MPC Controller .. Offset Performance in MPC CONTROL Systems .. Offset-free Performance Considering Inconstant Setpoint Tracking Challenges .. 223 CONTROL SYSTEM design AND implementation using DIS-CRETE INTEGRAL CONTROLLER .. Integral Controller Structure .. Offset-free Performance Analysis .. CONTROL Validation .. Example 1: Discrete SYSTEM from Ogata [1] .. Example 2: CSTR Plant from Seborg [2] .. Modeling equations .. Linearization of the SYSTEM .. Offset-free performance .. CONTROL Performance on the linear continuousand nonlinear CSTR systems .. 364 CONTROL SYSTEM design AND implementation using IN-CREMENTAL MPC.

3 Basic Equations for INCREMENTAL MPC .. CONTROL Validation .. Example 1: CSTR Plant from Seborg [2] .. Example 2: Quadruple Tank SYSTEM .. 5155 CONTROL SYSTEM design AND implementation using IN-TEGRAL MPC .. Basic Equations for Integral MPC .. First Integral Statez(k).. Second Integral Statew(k).. CONTROL Law for the Integral MPC Controller .. CONTROL Validation .. 586 CONCLUSIONS AND FUTURE WORK .. Conclusions .. Future Work: Constrained INCREMENTAL MPC .. 63 APPENDIXADERIVATION OFK1 ANDK2 FOR THE DISCRETE INTEGRAL CON-TROLLER .. 66 REFERENCES .. 70 BIOGRAPHICAL SKETCH .. 716 LIST OF TABLEST ablepage3-1 Plant parameters of the CSTR MODEL .. 344-1 Parameter values for the quadruple tank SYSTEM .. 524-2 Initial values of the quadruple tank SYSTEM .

4 527 LIST OF FIGURESF igurepage1-1 A typical block diagram of the closed-loop SYSTEM with a state feed-back integral controller.. 121-2 A block diagram of an MPC controller.. 143-1 Closed-loop matlab / simulink MODEL from Ogata s book, two differentdiscrete controllers(controller implemented by connected simulink blocks and by S-Function) are used .. 303-2 Offset-free CONTROL performance of the closed-loop SYSTEM with a dis-crete integral controller. The MODEL is example 6-12 in Ogata [1].. 323-3 The diagram of CSTR SYSTEM in the book .. 333-4 Offset-free performance of the linear discrete closed-loop CSTR sys-tem. There are step changes in setpoint, state disturbance and outputdisturbance at different time.. 373-5 CONTROL performance on the linear continuous CSTR SYSTEM using iden-tical step changes in setpoint, state disturbance and output disturbanceto those graphs in figure 3-4.

5 393-6 CONTROL performance on the nonlinear CSTR SYSTEM using the identi-cal step changes in setpoint, state disturbance and output disturbanceas subsection .. 404-1 CONTROL diagram of a closed-loop SYSTEM with the INCREMENTAL MPCcontroller .. 454-2 matlab / simulink diagram of the closed-loop SYSTEM with the incre-mental MPC controller .. 474-3 Simulation results for the linear discrete CSTR SYSTEM with the incre-mental MPC controller .. 484-4 Simulation results for the linear continuous CSTR SYSTEM with incre-mental MPC controller .. 494-5 Simulation results for the nonlinear CSTR SYSTEM with incrementalMPC controller .. 504-6 Process diagram for the quadruple tank SYSTEM from kesson [3].. 5284-7 Simulation results of INCREMENTAL MPC on the linearized quadrupletank SYSTEM . The CONTROL variable with its setpoint which is presentedin the top graph is the level for tank 1.

6 Two input variables denoted asu1andu2are shown in the bottom graph.. 545-1 Closed-loop simulation result for the linear discrete CSTR SYSTEM withan integral MPC controller. The CSTR is given by subsection the step changes are presented in subsection .. 595-2 Closed-loop simulation result for the linear continuous CSTR systemwith an integral MPC controller. The CSTR is given by subsection the step changes are presented in subsection .. 615-3 Closed-loop simulation result for the nonlinear CSTR SYSTEM with anintegral MPC controller. The CSTR is given by subsection andthe step changes are presented in subsection .. 629 Abstract of a Thesis Presented to the Graduate Schoolof the University of Florida in Partial Fulfillment of theRequirements for the Degree of Master of ScienceINCREMENTAL MODEL PREDICTIVE CONTROL SYSTEM design ANDIMPLEMENTATION using matlab /SIMULINKByXin LinMay 2013 Chair: Oscar.

7 D. CrisalleMajor: Chemical EngineeringThe integral and MODEL PREDICTIVE controller (MPC) drive controlled outputsto their desired targets, and this thesis addresses the problem of integral con-troller, INCREMENTAL and integral MPC when tracking the constant or inconstantreferences. design and implementation of the MPC under matlab / simulink en-vironment are discussed both in INCREMENTAL and integral form. Also one CSTR example is presented to compare the CONTROL performances among differentintegral controller and 1 INTRODUCTIONThis chapter is a guide to the topics covered by this thesis. The objectivesof this work include the design and implementation of integral and MPC con-trollers using S-functions under the matlab / simulink environment. Verificationand comparison of the CONTROL performance among different controllers controllers and the challenges associated with their design are de-scribed in Subsection An example for the plant and its detailed informationis provided in Subsections and , and the organization of this thesis isexplained in the description of contents Integral Controller and Offset-free PerformanceA closed-loop CONTROL diagram of an integral controller is shown in integral controller utilizes the plant states and plant outputs to calculateits output.

8 Such information can be directly measured or estimated by usingan observer. The design of an integral controller is simple and it uses an integral state, it can track constant setpoints without offset. Ifall the closed-loop poles are placed properly and the plant MODEL is accurateenough, the integral controller achieves the goals of both setpoint tracking anddisturbance disadvantage of the integral controller is that when there is strong interac-tion among different CONTROL loops, its performance will degrade , since it is designed based on a linear MODEL , it may fail to obtainsatisfactory CONTROL performance on the plant if there is serious MODEL mismatchor nonlinearity inside the 1-1. A typical block diagram of the closed-loop SYSTEM with a statefeedback integral Overview of MPCMPC is an optimal controller based on real-time numerical optimization.

9 Atypical MPC CONTROL diagram is given in figure 1-2. The plant output is predictedby using an estimated SYSTEM MODEL . The plant input is optimized at each timeinstantance according to penalty function and constraints. The main ideas ofMPC originally come from a computational technique used to improve controlperformance in process industries. Since then, PREDICTIVE CONTROL has became themost widespread advanced CONTROL strategy in chemical engineering. An MPCcontroller can achieve desired CONTROL performance in large-scale multivariablesystems, and provide a systematic method of dealing with states and inputsconstraints with simple design and general goal of MPC is to calculate a trajectory of future manipulatedvariableuto optimize the future behaviour of the plant outputy. The optimizationis carried out within a limited time instance by using the plant information at thestart of the time are three fundamental concept in the design of MPC.

10 The first is how topredict the future states and outputs ( MODEL ); the second is the way on how toobtain the current information of the plant (measurement) and the third is theapproach on the implementation of future activities (realization of CONTROL ). Thekey issues in the design time interval for the design is a constant; need to have access to the current states before the CONTROL design ; take the constraints into consideration, and the optimization isperformed in real-time with a time window that moves foward and with thelatest plant information concepts that are used frequently in the design of MPC are the following:the moving horizon window, prediction horizon, receding horizon CONTROL , andcontrol objective. They are discussed here as moving horizon window is referred to the time-dependent window froman arbitrary timetitoti+Tp. The length of the windowTpis a ,ti, which is the beginning of the window, depends on time andincreases as time prediction horizon determines how long into the future states andoutputs are to be predicted for.