Min, max selectors if then logics sequence logics other elements regulation constraint handling local optimization ad hoc strategies, heuristics inconsistent performance complex control. Minmax model predictive control of nonlinear systems using. Robust selftriggered minmax model predictive control for. This book was set in lucida using latex, and printed and bound by. Fontes and lalo magni abstract this paper proposes a model predictive control mpc algorithm. A complete solution manual more than 300 pages is available for course. Minmax model predictive control as a quadratic program article pdf available in iet control theory and applications 11. Minmax mpc using a tractable qp problem sciencedirect. Model predictive control mpc is a widely spread technology in industry for control design of highly complex multivariable processes. The remaining part of the thesis is devoted to stability issues in mpc for. Tutorial overview of model predictive control ieee control systems mag azine author.
Minmax is a way of organizing inventory replenishment using the following simple calculation. Minmax model predictive control mpc is one of the few techniques suitable for robust stabilization of uncertain nonlinear systems subject to constraints. Nonlinear model predictive control nmpc has become the accepted methodology to solve complex control problems related to process industries. Minmax model predictive control mpc is one of the few techniques suitable for. 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. Model predictive control mpc, also referred to asreceding horizon control and moving horizon optimal control, has been widely adopted in industry as an e ective means to deal with multivariable. Camachoefficient implementation of constrained minmax model predictive control with bounded uncertainties. Feedback minmax mpc obtains a sequence of feedback control laws that minimizes the. Pdf minmax model predictive control as a quadratic program. The basic idea in mpc is to repeatedly solve optimization problems online to nd an optimal input to the controlled system. Model predictive control mpc is a widely used control design method in the. Model predictive control mpc originated in the late seventies and has developed considerably since then. Minmax predictive control of a fivephase induction machine. Model predictive control for maxminplusscaling systems ef.
Tutorial overview of model predictive control ieee. A nonlinear model predictive control strategy is developed and subsequently specialized to autonomous aircraft that can be adequately modeled with a rigid 6degreesoffreedom representation. Robust selftriggered minmax model predictive control for discretetime nonlinear. Hi, i assume you are a masters student studying control engineering. An algorithm for explicit solution of minmax model. Robust and adaptive model predictive control of nonlinear. Predictive control with constraints maciejowski pdf download. Explicit nonlinear model predictive control theory and. Minmax model predictive control for uncertain maxmin. Hellendoorn if you want to cite this report, please use the following reference. To this end, we introduce a nonempty state con straint set x. The term model predictive control does not designate a specific control strategy but rather an. 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.
Lecture 12 model predictive control prediction model control optimization receding horizon update disturbance estimator feedback imc representation of mpc resource. Pdf minmax approaches to robust model predictive control. Minmax model predictive control of nonlinear systems using discontinuous feedbacks fernando a. A vertex rejection approach journal of process control, 15 2005. 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. Introduction to model predictive control springerlink. We extend mpc to maxminplus discrete event systems. The reorder quantity targets the max value for the new total stock level, hence the reorder quantity is the difference between max and min i. Minmax model predictive control implementation strategies eeci. Pdf minmax model predictive control of nonlinear systems.
The control optimization is therefore feasible for all initial states. Nonlinear model predictive control technique for unmanned. Predictive modeling tenets evaluate performance on a set of new cases test set should not be used in any step of building the predictive modeling model selection, parameter estimation avoid overfitting. Model predictive control mpc is an advanced method of process control that is used to control a process while satisfying a set of constraints. Minmax model predictive control for uncertain maxminplusscaling systems. If its is true, you may mostly refer books by camacho. Constraint satisfaction in current control of a fivephase drive with locally tuned. This text is an introduction to model predictive control, a control methodology which has encountered some success in industry, but which still presents many theoretical challenges. Prediction can be performed separately for frequency probability and severity loss. A block diagram of a model predictive control system is shown in fig.
Model predictive control advanced textbooks in control. Minmax mpc schemes can be classi ed in open loop and feedback minmax controllers see mayne et al. For all xt, we find, therefore, that the above set of controls satisfies the stability and state constraints. An introduction to modelbased predictive control mpc. Pdf minmax model predictive control mpc is one of the few techniques. Camacho minmax mpc 7 why minmax model predictive control. Predictive modeling is the process of estimating, predicting or stratifying members according to their relative risk. The idea behind mpc is to start with a model of the open. A minmax control framework, based on alternate minimisation. Model predictive control for maxminplusscaling systems.
What are the best books to learn model predictive control. It has been in use in the process industries in chemical. On robustness of suboptimal minmax model predictive control. Min value is the order point we are running out of the goods, while max is the quantity of the goods we need. In this paper, a fuzzylogic based operator is used instead of a traditional cost function for the predictive stator current control of a fivephase induction machine im. Series in engineering and computer science book series secs, volume 569. The main motivation behind explicit nmpc is that an. Their combined citations are counted only for the first article. Minmax model predictive control of nonlinear systems. The second edition of model predictive control provides a thorough introduction to theoretical and practical aspects of the most commonly used mpc strategies. Model predictive control for maxminplus systems springerlink.