An introduction to model based predictive control mpc by stanislaw h. Mpc controller together with their flood levels flood event. Model predictive control toolbox provides functions, an app, and simulink blocks for designing and simulating model predictive controllers mpcs. Tutorial 12 introduction the model predictive control mpc toolbox is a collection of functions commands developed for the analysis and design of model predictive control mpc systems. May 15, 2018 learn about the benefits of using model predictive control mpc. The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. It is aimed at readers with control expertise, particularly practitioners, who. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. Request pdf model predictive control of chemical processes. Model predictive control mpc, also known as receding horizon control or moving horizon control, uses the range of control methods, making the use of an explicit dynamic plant model to predict the effect of future reactions of the manipulated variables on the output and the control signal obtained by minimizing the cost function 7. The term model predictive control does not designate a specific control strategy but rather an ample range of control methods which make explicit use of a model of the process to obtain the control signal by minimizing an objective function.
Tutorial overview of model predictive control, ieee control systems magazine, vol. Control engineering 1520 industrial mpc features industrial strength products that can be used for a broad range of applications flexibility to plant size, automated setup based on step responseimpulse response model on the fly reconfiguration if plant is changing mv, cv, dv channels taken off control. Tutorial overview of model predictive control ieee. Theoretical aspects model predictive control mpc is a powerful control design method for constrained dynam. Model predictive control of hybrid systems ut yt hybrid system reference rt input output measurements controller model. Tutorial on model predictive control of hybrid systems. The paper provides a reasonably accessible and selfcontained tutorial exposition on model predictive control mpc. Model predictive control mpc originated in the late seventies and has developed considerably since then. Abstractthis tutorial paper surveys recent developments in parallel computer architecture, focusing on the fieldprogrammable gate array and the graphics processor. Introduction to model predictive control springerlink. Nlc with predictive models is a dynamic optimization approach that seeks to follow. Some simulation abilities were provided to simulate the closed loop performance of the controlled hybrid system. Model predictive control mpc has a long history in the field of control engineering. Concurrent learning adaptive model predictive control.
As we will see, mpc problems can be formulated in various ways in yalmip. A tutorial on model predictive control for spacecraft rendezvous. The architecture switches to onlinelearned model based model predictive control after an online automatic switch gauges the confidence in parameter estimates. Tutorial overview of model predictive control abstract. Model predictive control has had an exceptional history with early intimations in the academic literature coupled with an explosive growth due to its independent adoption by the process industries where it proved to be highly successful in comparison with alternative methods of multivariable control. Tutorial overview of model predictive control ieee control.
Model predictive control in labview 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 consideration e. Nlc with predictive models is a dynamic optimization. This example requires simulink control design software to define the mpc structure by linearizing a nonlinear simulink model. Blending system control ra and rb control q if possible flowratesof additives are limited classical solution mpc.
These properties however can be satisfied only if the underlying model used for. The residuals, the differences between the actual and predicted outputs, serve as the feedback signal to a. The general approaches of model predictive control and adaptive control provide benefits of robustness with respect to the model of internal dynamics. Model predictive control mpc is one of the most successful control. In recent years it has also been used in power system balancing models and in power electronics. It is one of the few areas that has received ongoing interest from researchers in both the industrial and academic communities. Ece7850 wei zhang ece7850 lecture 8 nonlinear model predictive control. Tutorial overview of model predictive control semantic scholar. It is aimed at readers with control expertise, particularly practitioners, who wish to broaden their perspective in the mpc area of control. Model predictive control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. Introduction to model predictive control mpc home pages of esat. The basic principles and theoretical results for mpc are almost the same for most nonlinear systems, including discretetime hybrid systems. Model predictive control in labview we set, and the mpc algorithm requires that the model is a linear statespace model, but the time delay causes problems.
Model predictive control in labview model predictive control mpc is a control strategy which is a special case of the optimal control theory developed in the 1960 and lather. We aim to illustrate the potential of these architectures for the. In the direct numerical optimal control literature, hicks and ray 1971. Model predictive control mpc is unusual in receiving ongoing interest in both industrial and academic circles. An introduction to modelbased predictive control mpc.
A tutorial on model predictive control for spacecraft rendezvous edward n. Ece7850 lecture 8 nonlinear model predictive control. May 30, 2018 model predictive control may be enhanced by adaptive feedback that modifies the parameters or the form for the model of internal dynamics. This is a linear feedback law so we can determine stability by checking the closed loop poles. Gives the human or philosophical thinking behind predictive control and explains why this is an intuitively obvious approach to control design. A tutorial on model predictive control for spacecraft. Model predictive control mpc, also referred to as receding horizon control, is an online optimizationbased control technique that optimizes a performance index or cost function over a prediction control horizon by taking advantage of a dynamic nominal process model i. In the supervisory control layer, model predictive control mpc e. Model predictive control provides high performance and safety in the form of constraint satisfaction. Its popularity steadily increased throughout the 1980s.
A process model is used to predict the current values of the output variables. This example shows how to design a model predictive controller for a continuous stirredtank reactor cstr in simulink using mpc designer. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closedloop stability and performance. It is aimed at readers with control expertise, particularly practitioners. A tutorial on model predictive control wiley online library. Model predictive control mpc was originally developed for relatively slow processes in the petroleum and chemical industries and is well known to have difficulties in computing control. The tutorial begins in section 2 with ob server based receding horizon control, in which we show that without using hard constraints the mpc system is. Blog archive 2020 433 2020 433 may 19 getting started with matlabsimulink. Learningbased model predictive control on a quadrotor. Rawlings rovicling a reasonrtlay accessible and selfcon. Model predictive control tutorial a basic model predictive control mpc tutorial demonstrates the capability of a solver to determine a dynamic move plan.
Model predictive control classical, robust and stochastic. Tutorial overview of model predictive control ieee control systems mag azine author. Theoretical aspects model predictive control mpc is a powerful control design method for constrained dynamical systems. Model predictive control is a family of algorithms that enables to. Model predictive control in labview model predictive control mpc is a control strategy which is a special case of the optimal control. To prepare for the hybrid, explicit and robust mpc examples, we solve some standard mpc examples. Chapter1 introductiontononlinearmodel predictivecontroland. An introduction to modelbased predictive control mpc by stanislaw h. The basic ideaof the method isto considerand optimizetherelevant variables, not. Control design for linear systems w constraints and hybrid systems on. Model predictive control mpc was originally developed for relatively slow processes in the petroleum and chemical industries and is well known to have difficulties in computing control inputs in real time for processes with fast dynamics. Mpc model predictive control also known as dmc dynamical matrix control gpc generalized predictive control rhc receding horizon control control algorithms based on numerically solving an optimization problem at each step constrained optimization typically qp or lp receding horizon control. Learn about the benefits of using model predictive control mpc.
This paper describes the parametrized derivativefree model predictive control pdf mpc package, a matlab coderbased set of subroutines that enables a model predictive control problem to be defined and solved. Apr 02, 2015 dynamic control is also known as nonlinear model predictive control nmpc or simply as nonlinear control nlc. Parallel architectures for model predictive control george a. The term model predictive control does not designate a specific control strategy but rather an ample range of control methods which make explicit use of a model of the process to obtain the control. A tutorial in this chapter, we will illustrate the ability of model predictive control mpc in dealing with the multivariable. Model predictive control was conceived in the 1970s primarily by industry. Dynamic control is also known as nonlinear model predictive control nmpc or simply as nonlinear control nlc.
A process model is used to predict the current values of the output. Issues such as plant optimization and constrained control which are critical. Lbmpc combines aspects of learningbased control and model predictive control mpc. Model predictive control mpc, also known as receding horizon control or moving horizon control, uses the range of control methods, making the use of an explicit dynamic plant model to predict the effect of future reactions of the manipulated variables on the output and the control. The idea behind this approach can be explained using an example of driving a car. The model predictive control mpc toolbox is a collection of functions. Model predictive control mpc is an advanced method of process control that is used to control a process while satisfying a set of constraints. To support the teaching and learning of mpc, we have included more than 200 endofchapter exer cises. Mpc consists of an optimization problem at each time instants, k. Technology is used not only for multivariable control but for most economic operation within constraint boundaries. Include explicitly in the problem formulation contraints on inputstateoutput variables, and also logic relations. Constantinides abstractthis tutorial paper surveys recent developments in parallel computer architecture, focusing on the. Model predictive control mpc unit 1 distributed control system pid unit 2 distributed control system pid fc pc tc lc fc pc tc lc unit 2 mpc structure. Model constraints stagewise cost terminal cost openloop optimal control problem openloop optimal solution is not robust must be coupled with online state model parameter update requires online solution for each updated problem analytical solution possible only in a few cases lq control.
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