An introduction to modelbased 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 signal obtained by minimizing the cost function 7. Chapter 3 nonlinear model predictive control in this chapter, we introduce the nonlinear model predictive control algorithm in a rigorous way. Our research lab focuses on the theoretical and realtime implementation aspects of constrained predictive modelbased control. This paper presents a selfscheduling mpc framework for plants described by lpv linear. It has been in use in the process industries in chemical.
Overall online approach with guaranteed stability and recursive feasibility. The actual costs of the closedloop system over a grid of initial points are depicted in the following figure. This paper presents a stabilizing tubebased mpc synthesis for lpv systems. The initial idcom and mpc algorithms represented the first generation of mpc. Introduction model predictive controller mpc is traced back to the 1970s. In order to address the engine limit protection problem more efficiently, a constrained fullstate model predictive controller mpc has been designed in this paper by incorporating a linear parameter varying. In section 6, the results of applying our novel approach to the wellknown. Leaving the technical details aside until chapter 3, this chapter will explain the basic idea of mpc and summarize. Model predictive control mpc is one of the most successful control techniques that can be used with hybrid systems. Finally the control performance of the explicit lpvmpc controller is compared to robust control and the truely optimal solution for a certain scheduling parameter trajectory for details see the. This thesis extends model predictive control mpc for constrained linear systems subject to. Predictive fault tolerant control for lpv systems using.
Model predictive control mpc can be an effective tool to design a gain scheduling policy for lpv systems. In sections 4 and 5, the technical details about h 2 h 1 control and e cient predictive control design for lpv systems are provided, respectively. Control of linear parameter varying systems with applications. Sep 16, 2016 finally the control performance of the explicit lpv mpc controller is compared to robust control and the truely optimal solution for a certain scheduling parameter trajectory for details see the above mentioned paper. Linear parametervarying control lpv control deals with the control of linear parametervarying systems, a class of nonlinear systems which can be modelled as parametrized linear systems whose. Onestep ahead model predictive control approach for lpv model with bounded disturbance. Pdf model predictive control of constrained lpv systems. Learn about the benefits of using model predictive control mpc. It started to emerge industrially in the 1980s as idcom richalet et.
This book provides elucidative information regarding model predictive control mpc. Instead, we use a soft sensor to estimate ca based on temperature measurements and the plant model. We deal with linear, nonlinear and hybrid systems in both small scale. Process control in the chemical industries 115 model predictive control an introduction 1. Model predictive control advanced textbooks in control and signal processing camacho, eduardo f.
Control c pid lag pid um model predictive mpc structure dynamic control fc pc tc lc fc pc tc lc control pid control pid effort models rather than first. 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. Savaresi, a hierarchical approach to datadriven lpv control design of constrained systems, in datadriven filter and control. In this paper, a gainscheduled, linear parameter varying lpv mpc law is considered that retains both computational efficiency and prediction fidelity. September 16, 2016 this example, contributed by thomas besselmann, accompanies the paper besselmann and lofberg 2008. Model predictive control mpc is an advanced method of process control that is used to control a process while satisfying a set of constraints. Control of linear parameter varying systems compiles stateoftheart contributions on novel analytical and computational methods for addressing system identification, model reduction, performance. Due to global competition, customers have more supply alternatives than ever before. Some simulation abilities were provided to simulate the closed loop performance of the controlled hybrid system. Model predictive control for lpv models with maximal.
However, todays applications often require driving the process over a wide region and close to the boundaries of erability, while satisfying constraints and achieving nearoptimal performance. Linear parametervarying control lpv control deals with the control of linear parametervarying systems, a class of nonlinear systems which can be modelled as parametrized linear systems whose parameters change with their state. Mpc model predictive control also known as dmc dynamical matrix control gpc generalized predictive control rhc receding horizon control control algorithms based on. From lower request of modeling accuracy and robustness to complicated process plants, mpc has been widely accepted in many practical fields. Adaptive mpc control of nonlinear chemical reactor using. Model predictive control offers several important advantages. This paper presents a model predictive control approach to discretetime linear parameter varying. From power plants to sugar refining, model predictive control mpc schemes have established themselves as the preferred control strategies for a wide variety of processes.
A method of nonlinear model predictive control based on an identified lpv model is proposed. 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 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. Finally, the proposed controller is applied to a wheelslip. The objective of this paper is to control the angular speed in a model of a dc motor using different control strategies like model predictive control and linear quadratic regulator for comparison. Half a century after its birth, it has been widely accepted in many engineering fields and has brought much. First, typical workingpoints are selected and linear models are identified using data sets at various workingpoints. Can anyone suggest me a book or tutorial for understanding model predictive control. What are the best books to learn model predictive control for. A survey on explicit model predictive control, in nonlinear model predictive control. First and foremost, the algorithms and highlevel software available for solving challenging nonlinear optimal control problems have advanced signi. Part of the nato asi series book series nsse, volume 353. Stability and optimality thomas besselmann, johan lo.
Anticipative model predictive control for linear parameter varying. Lpv model identification for gain scheduling control. We also assume that direct measurements of concentrations are unavailable or infrequent, which is the usual case in practice. Pdf in this paper, a discretetime model predictive control mpc design approach is proposed to control systems described by linear parametervarying.
In fact, bounds in parameter variations can be explicitly considered in the optimization step of mpc, guaranteeing stability, robustness and performances. In fact, bounds in parameter variations can be explicitly considered in the optimization step. Bs in information engineering, university of science and technology, beijing, 2008. You can link to the main page here, or find individual links for each video below this e book is intended to provide videos resources to assist you with your selfstudy for topics in model predictive control. As the guide for researchers and engineers all over the world concerned with the latest. Relevant features, such as recursive feasibility and stability of the lpvmpc, are theoretically proven. This paper characterizes model predictive control mpc for linear parameter varying lpv models subject to state and input constraints, which is based on the homogeneous polynomially parameterized hpp lyapunov function and hpp control law with tunable complexity degrees. Robust constrained model predictive control by arthur george richards submitted to the department of aeronautics and astronautics on november 22, 2004, in partial ful. Existing model predictive control schemes for control of. Pdf an mpc approach for lpv systems in inputoutput form. In process identification, a linear parameter varying lpv model approach is used.
Sep 16, 2016 as long as there are no efficient tools to compute explicit control laws for nonlinear discretetime systems directly, the control engineer is often left with the choice of either approximating a nonlinear system by a pwa model or to embed it in an lpv model, and use the techniques available for these systems. Model predictive control toolbox provides functions, an app, and simulink blocks for designing and simulating model predictive controllers mpcs. Note that the code below uses some awkward, no longer necessary, reformulations in order to cope with uncertainty in linear programming representable nonlinear terms. Onestep ahead robust mpc for lpv model with bounded.
Model predictive control of linear parameter varying systems. In sections 4 and 5, the technical details about h 2 h 1 control and e cient predictive. Since the beginning of the 1990s, a real boom in the number of industrial. The term model predictive control does not designate a specific control strategy but rather an. Robust linear parameter varying model predictive control and its. The toolbox lets you specify plant and disturbance. Model predictive control is that part of control algorithms in which a progressive method structure is utilized to foretell and improve process work.
Introduction to model predictive control springerlink. The basic ideaof the method isto considerand optimizetherelevant variables, not. Assessment and future directions of nonlinear model. Model based predictive and distributed control lab. Some simulation abilities were provided to simulate the closed loop performance of the controlled hybrid.
Model predictive control advanced textbooks in control and. 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. In this thesis, we deal with aspects of linear model predictive control, or mpc for short. In section 3, the proposed control architecture is explained. Predictive fault tolerant control for lpv systems using model. Stabilizing tubebased model predictive control for lpv systems. The second edition of model predictive control provides a thorough introduction to theoretical and practical aspects of the most commonly used mpc strategies. Publication date 2011 topics engineering, model control, software engineering. Can anyone suggest me a book or tutorial for understanding. The lpv systems under consideration depend on a set of parameters that are bounded and available online. O the basic concepts are introduced and then these are developed to. Naik, a numerically robust mixedinteger quadratic programming solver for embedded hybrid model predictive control, in 6th ifac conf.
Control of linear parameter varying systems compiles stateoftheart contributions on novel analytical and computational methods for addressing system identification, model reduction, performance analysis and feedback control design and addresses address theoretical developments, novel computational approaches and illustrative applications to. An overview of some recent developments in the area is found in the book 10. May 15, 2018 learn about the benefits of using model predictive control mpc. 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. Part of the lecture notes in computer science book series lncs, volume 8890. Hi, i assume you are a masters student studying control engineering. The idea behind this approach can be explained using an example of driving a car. You can link to the main page here, or find individual links for each video below this e book is intended to provide videos resources to assist you. The rockwell automation model predictive control delivers customer value. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. Model predictive control mpc refers to a class of control algorithms in which a dynamic process model is used to predict and optimize process performance.
For this reason, we have added a new chapter, chapter 8, numerical optimal control, and coauthor, professor moritz m. If its is true, you may mostly refer books by camacho. Robust constrained model predictive control arthur george. An introduction to modelbased predictive control mpc by stanislaw h. The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. However, todays applications often require driving the process over a wide. Nonlinear mpc using an identified lpv model industrial. Energies free fulltext design of disturbance extended. As long as there are no efficient tools to compute explicit control laws for nonlinear discretetime systems directly, the control engineer is often left with the choice of either approximating. Sep, 2016 hi, i assume you are a masters student studying control engineering. I want to understand mpc and its basics mathematics and application. Model predictive control mpc originated in the late seventies and has developed considerably since then.