Model predictive control tuning by inverse matching for a. This paper presents the employment of a model based predictive control algorithm for the energy efficient temperature control of a solarthermal system consisting of a solar collector and a heat. Model predictive control mpc originated in the late seventies and has developed considerably since then. Each of these techniques offers advantages with respect to disturbance rejection. Model predictive control offers several important advantages. As the guide for researchers and engineers all over the world concerned with the latest. The second edition of model predictive control provides a thorough introduction to theoretical and practical aspects of the most commonly used mpc strategies. Accordingly, automotive control has been increasing its authority and. Generalized predictive control and neural generalized. We 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. This model is known as a controller autoregressive movingaverage carima model. This is a vector space also known as a linear space. Introduction to model predictive control springerlink. We present a new timecoarsening strategy for model predictive control mpc that we call diffusinghorizon mpc.
In fact, mpc is a solid and large research field on its own. Convex optimization, stephen boyd and lieven vandenberghe, 2004 cambridge university press. 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. Predictive control is a way of thinking not a specific algorithm. Reduction of energy demand in paper making using online. 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. This introduction only provides a glimpse of what mpc is and can do. The book proposes a simple predictive controller where the control laws are given in clear text that requires no calculations. Introduction model predictive control mpc is an industry accepted. Three major aspects of model predictive control make the. Setpoint tracking problem so far, we have only considered the problem of regulating the states and inputs around the origin. Bs in information engineering, university of science and technology, beijing, 2008.
Engineering tripos part iib module 4f3 nonlinear and. Engineering tripos part iib module 4f3 nonlinear and predictive control handout 1. Constraints on system variables in practice, system variables are always constrained by. Model predictive control mpc has a long history in the field of control engineering. 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. Leaving the technical details aside until chapter 3, this chapter will explain the basic idea of mpc and summarize the content of the thesis. Model predictive control is a kind of modelbased control design approach. In the field of electric drive control, whitebox wb modeling approaches derived from firstorder. Control engineering 143 receding horizon control at each time step, compute control by solving an openloop optimization problem for the prediction horizon apply the first value of the computed control sequence at the next time step, get the system state and recompute future input trajectory predicted future output plant model. Predictive control with constraints maciejowski pdf download. A provoking analogy between mpc and classical control can be found in 15. The main motivation for this paper is to demonstrate that model predictive control mpc can be an attractive feedback control approach for rpo maneuvering, which has traditionally been.
It is one of the few areas that has received ongoing interest from researchers in both the industrial and academic communities. From lower request of modeling accuracy and robustness to complicated process plants, mpc has been widely accepted in many practical fields. Model predictive control mpc is a modern control strategy known for its capacity to provide optimized responses while accounting for state and input constraints of the system. This paper presents a tutorial survey of model predictive control for constrained linear plants and nonlinear plants. Mayne, 2009 nob hill publishing predictive control with constraints, jan maciejowski, 2000 prentice hall optimization. Circle and popov criteria 1 introduction the stability criteria discussed in these notes are. Pdf probability density function qp quadratic programming.
Ee392m winter 2003 control engineering 121 lecture 12 model predictive control prediction model control optimization receding horizon update disturbance estimator feedback imc representation of mpc resource. Model predictive control linear convex optimal control. This video continues the previous by breaking down the thinking into the different aspects which underpin a. The past though the ideas of receding horizon control and model predictive control can be traced back to the 1960s garc. Predictive control without constraints predictive control with constraints stability and feasibility in predictive control setpoint tracking and offsetfree control industrial case study dr paul austin fri. A complete solution manual more than 300 pages is available for course. An introduction to modelbased predictive control mpc by stanislaw h. As specific objectives, by the end of the course students should be able to. Predictive control is aimed at students wishing to learn predictive control, as well as teachers, engineers and technicians of the profession. Infinitehorizon differentiable model predictive control. Publication date 2011 topics engineering, model control, software engineering publisher intech collection. The basic ideaof the method isto considerand optimizetherelevant variables, not only at the current time point but also during their course in the future. Generalized predictive control and neural generalized predictive control sadhana chidrawar, balasaheb patre 6 where, is the dead time of the system.
Optimal and predictive control part iib module 4f3 see also seminars and lectures. Model predictive control college of engineering uc santa barbara. The idea behind this approach can be explained using an example of driving a car. Engineering tripos part iib module 4f3 nonlinear and predictive control handout 4. Model predictive control for complex trajectory following. How to emplement model predictive control discussion. Model predictive control utcinstitute for advanced. 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.
Thus, by repeatedly solve an openloop optimization problem with every initial conditions updated at each time step, the model predictive control strategy results in a closedloop constrained optimal control technique. Unesco eolss sample chapters control systems, robotics and automation vol. Pdf modelbased predictive control of electric drives. Stability of the rhc law depends on the proper choice of the parameters q, r, p and n. Control design methods based on the mpc concept have found wide acceptance in industrial applications and have been studied by academia. Spacecraft rendezvous control problems have received signi. This paper proposes a model predictive control strategy for an organic rankine cycle based waste heat recovery system. Automotive model predictive control models, methods and.
If n 1, we will drop the subscript and write just r. Pdf model based predictive control for a solarthermal. Model predictive control 3 main components continued. The performance of model predictive controllers mpc strongly depends on the model quality. In this thesis, we deal with aspects of linear model predictive control, or mpc for short. An introduction to modelbased predictive control mpc.
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