Course on model predictive control part ii linear mpc design gabriele pannocchia department of chemical engineering, university of pisa, italy email. Model predictive control theory and design by james b. Model predictive control toolbox provides functions, an app, and simulink blocks for designing and simulating model predictive controllers mpcs. A survey of industrial model predictive control technology. Model predictive control mpc is an advanced method of process control that is used to control a process while satisfying a set of constraints. Half a century after its birth, it has been widely accepted in many engineering fields and has. An introduction to modelbased predictive control mpc by stanislaw h. Control theory computation and design model predictive control theory computation and design 2nd edition rawlings james b mayne david q diehl moritz m download bok download books for discrete time model predictive control attention is focused on the design. Model predictive control college of engineering uc santa barbara. The model predictive control technology is used to steer processes closer to their physical limits in order to obtain a better economic result. For the last 30 y ears the theory and technology of model predictive control mpc hav e.
The mpc calculations are based on current measure ments and predictions of the future values of the out puts. Morari, 2017 cambridge university press model predictive control. Process control in the chemical industries 115 model predictive control an introduction 1. The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights.
With the advent of affordable and fast computation, control engineers now need to think about using computationally intensive controls, so the second part of this book addresses the solution of optimization problems in real time for modelpredictive control. In recent years it has also been used in power system balancing models and in power electronics. Model predictive control university of connecticut. Here we extend ihmpc to tackle periodic tasks, and demonstrate the power of our approach by synthesizing hopping behavior in a simulated robot. Model predictive control mpc is a form of online optimal control in which the control action is obtained by solving, at each sampling instant, a finite horizon optimal control problem in which the initial state is the current state of the plant 6. Outline 1 estimator module design for offsetfree tracking 2 steadystate optimization module design 3 dynamic optimization module design 4 closedloop implementation and receding horizon principle 5 quick overview of numerical optimization g. Switched model predictive control of switched linear systems. Feasibility, stability and robustness lixian zhang, songlin zhuang, richard d. Model predictive control is a flexible paradigm that defines the control law as an optimization problem, enabling the specification of timedomain objectives, high performance control of complex multivariable systems and the ability to explicitly. The goal of this postface is to point out and comment.
Theory, computation, and design, 2nd edition rawlings james b. Rawlings department of chemical engineering university of california santa barbara, california, usa david q. Tutorial overview of model predictive control ieee. Theory can augment human judgement and intuition in the development and implementation of better mpc. A critical synthesis of theory and industrial needs michael nikolaou1. 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.
It started to emerge industrially in the 1980s as idcom richalet et. See all formats and editions hide other formats and editions. Request pdf on jan 1, 2009, j b rawlings and others published model predictive control. Predictive control for linear and hybrid systems, f. Controlling largescale systems with distributed model. We describe the basic functionality of mpc controllers, their properties regarding feasibility, stability and performance and the assumptions needed in order to rigorously ensure these properties in.
Half a century after its birth, it has been widely accepted in many engineering fields and has brought much. Process control, and serves as a trustee of the computer aids in chemical engineering cache corporation. An introduction to modelbased predictive control mpc. Control design methods based on the mpc concept have found wide acceptance in industrial applications and have been studied by academia. Model predictive control workshop 2015 american control. The idea behind this approach can be explained using an example of driving a car.
Model predictive control mpc originated in the late seventies and has developed considerably since then. Tutorial overview of model predictive control ieee control systems mag azine author. Based on a generated model of this system, we design a set of control input sequences iteratively at successive time steps over some horizon from a. A block diagram of a model predictive control system is shown in fig. Model predictive control institute for dynamic systems. Pdf model predictive control status and challenges. Nob hill publishing is pleased to announce the availability of the second edition of the textbook, model predictive control. Introduction model predictive controller mpc is traced back to the 1970s. Model predictive control classical, robust and stochastic. A summary of each of these ingredients is given below. Altmdwe refer to model predictive control mpc as that family of controllers in which there is a direct use of an explicit and separately identifiable model. Zheng, nonlinear model predictive control, springerverlag, 2000. Diehl, 2017 nob hill publishing receding horizon control, w. Control design methods based on the mpc concept have found wide acceptance in industrial applications and.
We describe the basic functionality of mpc controllers, their properties regarding feasibility, stability and performance and the assumptions needed in order to rigorously ensure these properties in a nominal setting. 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. Introduction, dynamic modeling, predictive control versus classical pid control. Model predictive control may be enhanced by adaptive feedback that modifies the parameters or the form for the model of internal dynamics. Model predictive control institute for dynamic systems and. Course on model predictive control part ii linear mpc design. Rawlings, university of university of california, santa barbara, david q. Lecture 1 introduction to mpc motivation history and status of industrial use of mpc. Set of initial states leading to feasible closedloop trajectories. Controlling largescale systems with distributed model predictive control james b. To be meaningful, any statement about \robustness of a particular control algorithm must make reference to a speci c uncertainty range 1 morari 1994 reports that a simple database search for \ predictive control generated 128 references for the years 19911993. These properties however can be satisfied only if the underlying model used for prediction of. Mayne department of electrical and electronic engineering imperial college london london, england moritz m. The two days of lectures will cover the following topics.
A process model is used to predict the current values of the output variables. To be meaningful, any statement about \robustness of a particular control algorithm must make reference to a speci c uncertainty range 1 morari 1994 reports that a simple database search for \predictive control generated 128 references for the years 19911993. Outline 1 overview of distributed model predictive control control of largescale systems 2 cooperative control stability theory for cooperative mpc 3 conclusions and future outlook 4 some comments on tom edgar rawlings distributed mpc 2 23. The residuals, the differences between the actual and predicted outputs, serve as the feedback signal to a.
Model predictive control 3 variantsofmpc trackingmpc. Model predictive control system design and implementation using matlab, by wang, liuping model predictive control. Linear mpc refers to a family of mpc schemes in which linear models are used to predict the system dynamics and considers linear constraints on the states and inputs. Theory and design find, read and cite all the research you need.
Model predictive control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. Tutorial overview of model predictive control ieee control. Model predictive control status and challenges y ugeng xi 1 dew ei li 1 shu lin 1 abstract. This text provides a comprehensive and foundational treatment of. Model predictive control control theory mathematical. Model predictive control is a controller design method which synthesizes a sampled data feedback controller from the iterative solution of open loop optimal control problems. Introduction to model predictive control springerlink. Theory, computation, and design, 2nd edition hardcover october 1, 2017 by james b. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closedloop stability and performance. 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.
Model predictive control linear convex optimal control. Model predictive control theory and design rawlings, james b. The objective of the mpc control calculations is to determine a sequence of. The initial idcom and mpc algorithms represented the first generation of mpc. Highperformance model predictive control for process industry. Model predictive control provides high performance and safety in the form of constraint satisfaction. Description model predictive control is a flexible paradigm that defines the control law as an optimization problem, enabling the specification of timedomain objectives, high performance control of complex multivariable systems and the ability to explicitly enforce constraints on system behavior. Department of electrical and electronic engineering. Iterative activeset method for efficient online mpc design. Model predictive control mpc usually refers to a class of control algorithms in which a dynamic process model is used to predict and optimize process performance, but it is can also be seen as a term denoting a natural control strategy that matches the human thought form most closely. Mayne, 2009 nob hill publishing predictive control with constraints, jan maciejowski, 2000 prentice hall. Model predictive control theory computation and design. Theory, computation, and design, 2nd edition james b. By running closedloop simulations, you can evaluate controller performance.
The general approaches of model predictive control and adaptive control provide benefits of robustness with respect to the model of internal dynamics. Theory stability proofs etc lagging behind 1020 years. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. Can anyone suggest me a book or tutorial for understanding. While engineering intuition may frequently be used in the analysis of the behavior or in the design of mpc systems, theory can provide valuable help.
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