Jun 29, 2017 design and simulate model predictive controllers with model predictive control toolbox deploy model predictive controllers by either generating c code with simulink realtime or by using. Industrial mpc prediction model control optimization receding horizon update disturbance estimator feedback imc representation of mpc. The predictive controller is the infinite horizon model predictive control ihmpc, based on a statespace model that that does not require the use of a state observer because the nonminimum state is built with the past inputs and outputs. This method allows output tracking over a finite horizon when constraints are considered and equilibrium tracking in the linearized dynamics after that for the infinite horizon. Model predictive control part i utsa college of engineering.
Choose sample time and horizons sample time duration. The basic ideaof the method isto considerand optimizetherelevant variables, not. A numerical example is used to illustrate the proposed control algorithm. Tutorial overview of model predictive control ieee control systems. Pdf a quasiinfinite horizon nonlinear model predictive. Rawlings rovicling a reasonrtlay accessible and selfcon tained tutorial expositinn on mutlei prcclictivc control mpc is the puipose ni this article. The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. Use a dynamical model of the process to predict its future. In this course, we will talk about predictive control. Model predictive control in this chapter we consider model predictive control mpc, an important advanced control technique for dif. In contrast to the standard, discretetime nitehorizon model. I also called model predictive control mpc for reasons well see later 5. The infinite horizon cost is enforced using a terminal cost function.
A novel formulation for infinite horizon model predictive control that is general and can accurately approximate and minimize the control cost is proposed. Here we extend ihmpc to tackle periodic tasks, and demonstrate the power of our approach by synthesizing hopping behavior in a simulated robot. Special section industrial process control by james b. This paper proposes a differentiable linear quadratic model predictive control mpc framework for safe imitation learning. Pdf we present a method that combines offline trajectory optimization and online model predictive control mpc, generating robust. Dynamic matrix control dmc is a concrete type of model predictive control mpc, which is a set of advanced control schemas, i. Model predictive control roshan sharma october 2019. The explicit solution of model predictive control via. A typical bottleneck of model predictive control algorithms is the computational burden in order to compute the receding horizon feedback law which is predominantly determined by the length of the prediction horizon. An introduction to modelbased predictive control mpc by stanislaw h. Introduction to model predictive control toolbox youtube. Online optimization is possible because this class of problems is relatively easy to solve, but may.
In this chapter we give an introduction to nonlinear infinite horizon optimal control. N tend to infinity in the objective function of eq. Nlc with predictive models is a dynamic optimization approach that seeks to follow. We predict the behavior of a process state output over a time horizon. One of the main results of this chapter is that the infinite horizon optimal feedback law asymptotically stabilizes the system and that the infinite horizon. We present a structured interiorpoint method for the efficient solution of the optimal control problem in model predictive control.
A quasiinfinite horizon nonlinear model predictive. The consequences of applying the parametric description to receding horizon control model predictive control are discussed. Hv ac contr ol using infinitehorizon economic mpc david i. Model predictive control receding horizon control goal start chance to adapt on the fly. Robust constrained model predictive control using linear. We focus on formulating mpc as an infinite horizon optimal control strategy with a quadratic performance criterion. The repeated online solution of the finitehorizon problem for an everreceding horizon is called model predictive control. In the first stage of this work part i, an infinite horizon model predictive controller ihmpc using the closedloop paradigm, was adapted to be applied to batch processes. If one chooses to hold the prediction horizon duration the product pt s constant, p must vary inversely with t s. Model predictive control historically 1980s came about as a controller form, from the level of accuracy of mathematical models scientist and engineers have been able to come up with over the years. This paper presents an inverse optimal control methodology with its application to training a predictive model of human motor control from a manipulation task.
Model predictive control mpc approach is selected as the. To prepare for the hybrid, explicit and robust mpc examples, we solve some standard mpc examples. Model predictive control certaintyequivalent control constrained linearquadratic regulator in nite horizon model predictive control mpc with disturbance prediction. Three decades have passed since milestone publications by several industrialists spawned a flurry of research and industrial commercial activities on model predictive control. Introduction to model predictive control springerlink. In this course, we will talk about predictive control in detail throughout the semester.
Introduction model predictive control mpc is the most popular advanced control technique in the process industry. Tutorial overview of model predictive control ieee. Model predictive control mpc, also known as moving horizon control i\iiic or receding horizon control rhc, is a popular technique for the control of slow dynamical systems, such as those encountered in chemical process control in the petrochemical, pulp and paper industries, and in gas pipeline control. Here at each sampling time, starting at the current state, an openloop optimal control problem is solved over a fi nite horizon.
Index terms control system synthesis, model predictive control, infinite horizon optimal control, thermal variables control. Key words asymptotic stability, feedback, model predictive control algorithm, performance, receding horizon control. Introduction to model predictive control riccardo scattoliniriccardo scattolini dipartimento di elettronica e informazione. Pdf infinitehorizon model predictive control for periodic tasks. It introduces a convex formulation for learning both objective function and constraints of an infinite horizon constrained optimal control. Infinitehorizon model predictive control for periodic. Pdf infinitehorizon differentiable model predictive control. Model predictive control certaintyequivalent control constrained linearquadratic regulator in nite horizon model predictive control mpc with disturbance prediction 1. Model predictive control mpc originated in the late seventies and has developed considerably since then.
Model predictive control linear convex optimal control. In particular, recursive feasibility and closedloop stability are shown. Infinite horizon economic model predictive control. Tutorial overview of model predictive control ieee control systems mag azine author. Tutorial overview of model predictive control, ieee control. Suppose that we wish to control a multipleinput, multipleoutput process while satisfying inequality constraints on the. Stability of model predictive control riccardo scattoliniriccardo scattolini. The basic mpc concept can be summarized as follows. This thesis is devoted to apply a model predictive fuzzy controller mpc and takagisugeno to a specific steam boiler plant. Fast model predictive control with long prediction horizon jiri burant april 2017 advisor. Worstcase formulations of model predictive control for. A quasiinfinite horizon nonlinear model predictive control scheme. This controller corresponds to an infinite horizon control law. Model predictive control toolbox getting started guide.
The ability to cope with various operating conditions and to explicitly. An introduction to model based predictive control mpc by stanislaw h. Parametrized infinitehorizon model predictive control for linear. Control engineering 1521 technical detail tuning of mpc feedback control performance is an issue. 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. Model predictive control college of engineering uc santa barbara. An introduction to modelbased predictive control mpc. Based on these results, a procedure for systematically determining a terminal region and a terminal. Difference between model predictive control and rolling horizon optimization. Tutorial overview of model predictive control, ieee control systems magazine, vol. Recommended practice is to choose the control interval duration controller property t s initially, and then hold it constant as you. As we will see, mpc problems can be formulated in various ways in yalmip. This is perhaps a more appropriate prediction horizon because chemical.
Predictive control, robust stability, infinite horizon. Pdf control design often seeks the best trajectory along which to move a system from its current state to a target state. Impulse and step response models and the prediction equation. A quasi infinite horizon nonlinear model predictive control scheme with guaranteed stability. Tutorial on model predictive control of hybrid systems. Model predictive control toolbox provides functions, an app, and simulink blocks for designing and simulating model predictive controllers mpcs. A complete solution manual more than 300 pages is available for course instructors. At each time step, a finite horizon forecast is made from a predictive model of the system and the optimal. The controller considers the existence of zone control. Tutorial overview of model predictive control ieee control. 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 general infinite horizon mpc method in this article extends current theory by allowing cost to simultaneously penalize control energy, control rates, and output tracking all over an infinite horizon. Trajectories, cost function, dynamic model, constraints l i m i t c y c l e finite horizon implementation without a proper infinite horizon.
A quasi infinite horizon nonlinear model predictive control scheme 1207 state feedback. On the influence of the prediction horizon in dynamic matrix. However, implementation of empc can result in unexpected and, at times, pathological closedloop behavior, including inventory creep and bangbang actuation. This creates a feedback action and is commonly known as a sliding horizon strategy.
To address closedloop stability, one may consider employing an infinite horizon in the empc of eq. Pdf infinitehorizon model predictive control for periodic. Model predictive control control theory mathematical. This combination of an offline solution of the infinite horizon problem with an online mpc controller is known as infinite horizon model predictive control ihmpc, and has previously been applied. Solve a finite or infinite horizon openloop optimal control. An infinite horizon model predictive control for stable and integrating processes. To prepare for the hybrid, explicit and robust mpc. Dual mode control infinite horizon terminal constraint set. Infinitehorizon differentiable model predictive control. Model predictive control of hybrid systems ut yt hybrid system reference rt input output measurements controller model. The work begins with an illustration of economic model predictive control empc as a vehicle to exploit dispatch capabilities by pursuing the objective of maximizing revenue directly. Model predictive control toolbox product description design and simulate model predictive controllers model predictive control toolbox provides functions, an app, and simulink blocks for systematically analyzing, designing, and simulating model predictive controllers. Stability and performance guarantees for model predictive.
Pavel otta czech technical university in prague faculty of electrical engineering, department of control engineering. Keywords model predictive control, dynamic matrix control, prediction horizon. Fast model predictive control with long prediction horizon. Igcc power plant dispatch using infinitehorizon economic. Markov decision processes and exact solution methods. Chapter 3 nonlinear model predictive control in this chapter, we introduce the nonlinear model predictive control algorithm in a rigorous way. Approach called model predictive control also known as receding horizon control. Introduction model predictive control refers to a control strategy in which a model of the process is used to predict its future behaviour. 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.
Batch processes fall into the category of finiteduration systems, where the control. The first decade is characterized by the fastgrowing industrial adoption of the. Infinite horizon model predictive control robotics. This combination of an offline solution of the infinite horizon problem with an online mpc controller is known as infinite horizon model predictive control. Parametrized infinitehorizon model predictive control for. This combination of an offline solution of the infinite horizon problem with an online mpc controller is known as infinite horizon model predictive control ihmpc, and has previously. Estimates on the prediction horizon length in model predictive control 3. Infinite horizon model predictive control tracking. Although different fields have adopted different names and favorite model forms, the underlying concept is the same. Finally, we use this quadratic approximation as the terminal cost of an online mpc. Application of interiorpoint methods to model predictive control1 c. This paper provides a brief tutorial on model predictive control mpc theory for linear and nonlinear models.
Model predictive control mpc has become the ac cepted standard for complex constrained multivariable control problems in the process industries. In model predictive control, the prediction horizon, p is also an important consideration. A quasi infinite horizon nonlinear model predictive control scheme with guaranteed stabilitycontrol scheme with guaranteed stability, automaticavol34n10ppautomatica, vol. This paper presents an infinitehorizon differentiable linear quadratic mpc that can be learned. 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. Works in practice, without formal analysis theory requires large infinite prediction horizon or terminal constraint additional tricks for a separate static optimization step integrating and unstable dynamics. The idea behind this approach can be explained using an example of driving a car. Pdf an infinite horizon model predictive control for. Three decades have passed since milestone publications by several industrialists spawned a flurry of research and industrial commercial activities on model predictive control mpc. Model predictive control mpc is a modern controller which is widely used in industrial. The dynamic programming principle as well as several consequences of this principle are proved. The cost of this approac h is linear in the horizon. Apr 02, 2015 dynamic control is also known as nonlinear model predictive control nmpc or simply as nonlinear control nlc. 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.
In other words, let n tend to infinity similarly, let. The discussion of mpc with linear models covers the topics of steadystate target calculation, infinite horizon receding horizon. See this paper for the precise problem formulation and meanings of the algorithm parameters. Application of interiorpoint methods to model predictive. You can specify plant and disturbance models, horizons, constraints, and. The output desired to track can vary arbitrarily over a finite horizon and can take any value that corresponds to an equilibrium after that.
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