Diehl, 2017 nob hill publishing receding horizon control, w. In stochastic model predictive control wt is a random process, a sequence of independent, identically distributed random. The performance objective of a model predictive control algorithm determines the optimality, stability and convergence properties of the closed loop control law. This chapter considers several formulations and solutions of smpc problems and discusses some examples and applications in this diverse, complex, and growing field. Chapter1 introductiontononlinearmodel predictivecontroland. Classical, robust and stochastic advanced textbooks in control and signal processing 9783319248516 by kouvaritakis, basil. Stabilizing model predictive control of stochastic. Perhaps a key reason for this lacuna is the fact that the technicalities involved in stochastic model predictive.
Abstract in this paper we propose a stochastic model predictive control mpc formulation based on scenario generation for linear systems affected by discrete multiplicative disturbances. Although the roots of mpc go back to the early 1960s, a remarkable surge in its popularity. Model predictive control classical, robust and stochastic. Model predictive control college of engineering uc santa barbara. Smpc strategies make use of the probabilistic description of uncertainty to define chance constraints which allow a certain admissible. Pdf on nov 1, 2018, agustina d jorge and others published robust and stochastic mpc for tracking. Chapter 22, sampling and filtering of continuous measurements. Scenariobased model predictive control of stochastic. In the direct numerical optimal control literature, hicks and ray 1971.
Robust nonlinear model predictive control of batch processes. This volume provides a definitive survey of the latest modelpredictive control methods available to engineers and scientists today. Stochastic model predictive control for probabilistically constrained markovian jump linear systems with additive disturbance. Model predictive control for stochastic systems by. A stochastic model predictive control smpc design approach is proposed to optimize closedloop performance while enforcing constraints. Recent developments in modelpredictive control promise remarkable opportunities for designing multiinput, multioutput control systems and improving the control of singleinput, singleoutput systems. Nmpc schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate. Mark cannon for the first time, a textbook that brings together classical predictive control with treatment of uptodate robust and stochastic techniques. Robust nonlinear model predictive control of batch processes zoltan k.
Application of robust model predictive control to a renewable hydrogenbased microgrid p. Cannon, mark and a great selection of similar new, used and collectible books available now at great prices. Stochastic model predictive control how does it work. A summary of each of these ingredients is given below.
Stochastic model predictive control pantelis sopasakis imt institute for advanced studies lucca february 10, 2016. Predictive control for linear and hybrid systems, f. Advanced textbooks in control and signal processing basil kouvaritakis mark cannon model predictive control classical, robust and stochastic advanced. Stochastic nonlinear model predictive control with e cient. Model predictive control under uncertainty is an active research area that has received tremendous attention in the recent past, with developments in several di erent approaches to robust and stochastic mpc in the control literature. Stochastic multiobjective economic model predictive. Advanced textbooks in control and signal processing model predictive control basil kouvaritakis mark cannon classical, robust and stochastic. Openloop optimization strategies robust model predictive control with additive uncertainty.
While deterministic and robust versions of model predictive control techniques are standardized and well documented today, 1 stochastic versions still suffer from the absence of a comprehensive, unified, and systematic treatment. Datadriven scenario selection for multistage robust model. Bordons abstractin order to cope with uncertainties present in the renewable energy generation, as well as in the demand consumer, we. Morari, 2017 cambridge university press model predictive control. Model predictive control mpc is an advanced method of process control that is used to control a process while satisfying a set of constraints. A key feature of smpc is the inclusion of chance constraints, which enables a systematic tradeoff between attainable control performance and probability of state constraint violations in a stochastic setting.
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. Discrete modeling and control from the first edition of process dynamics and control by dale seborg, tom edgar, and duncan mellichamp. Pdf advanced textbooks in control and signal processing model. Many of these approaches solve an open loop optimization problem to determine the optimal control. In recent years it has also been used in power system balancing models and in power electronics.
By separating the problems of 1 stochastic performance, and 2 stochastic stabilization and robust constraints. Stochastic model predictive control ali mesbah, ilya kolmanovsky and stefano di cairano i. Model predictive control mpc has become a dominant advanced control framework that has made a tremendous impact on both the academic and industrial contr model predictive control. Model predictive control classical, robust and stochastic basil. We model the preceding vehicle as an autonomous stochastic system, using a hybrid model with continuous dynamics and discrete, markovian inputs. This thesis aims at developing economic mpc econ mpc strategies to optimize and control the nonlinear mechanical pulping mp process with two high consistency hc refiners, which is one of the most energy intensive processes in the pulp and paper industry.
Competing methods for robust and stochastic mpc sciencedirect. Model predictive control describes the development of. Classical, robust, and stochastic bookshelf model predictive control mpc has become a dominant advanced control framework that has made a tremendous. In this section we consider how to generalize the quadratic cost typically employed in linear optimal control problems to. This book offers readers a thorough and rigorous introduction to nonlinear model predictive control nmpc for discretetime and sampleddata systems. Introduction classical model predictive control robust model predictive control with additive uncertainty. Classical, robust, and stochastic bookshelf article in ieee control systems 366. Application of robust model predictive control to a. Read download predictive control with constraints pdf. Stochastic model predictive control basil kouvaritakis mark cannon abstract model predictive control mpc is a control strategy that has been used successfully in numerous and diverse application areas. Classical, robust and stochastic advanced textbooks in control and signal processing at. The aim of the present article is to discuss how the basic ideas of mpc can be.
We propose a learningbased, distributionally robust model predictive control approach towards the design of adaptive cruise control acc systems. Varying model has been proposed for adaptive mpc with robust constraints. Advanced textbooks in control and signal processing, springer, 2015. Stochastic model predictive control without terminal constraints. Model predictive control mpc has attracted considerable research efforts and has been widely applied in various industrial processes. We discuss implications for the convergence of control laws. Stochastic predictive control wiley online library. A numerically tractable stochastic model predictive control smpc strategy using conditional value at risk cvar optimization for discretetime linear timeinvariant systems, with state and input constraints, subject to additive uncertainty, is presented. Model predictive controllers rely on dynamic models of. Introduction stochastic model predictive control smpc accounts for model uncertainties and disturbances based on their statistical description. Model predictive control for stochastic systems by randomized algorithms citation for published version apa. Optimal control theory is a systematic approach to controller design whereby the desired performance objectives are encoded in a cost function, which is subsequently optimized to determine the desired controller. Learningbased riskaverse model predictive control for.
Model predictive control for stochastic systems by randomized algorithms. Tractable stochastic model predictive control using. Stochastic model predictive control smpc accounts for model uncertainties and disturbances based on their probabilistic description. In stochastic model predictive control wt is a random process, a sequence of independent, identically distributed random variables taking values in a set w. The starting point is classical predictive control and the appropriate. In robust model predictive control it is assumed that the disturbance w takes values in the compact set w. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. Stochastic model predictive control smpc provides a probabilistic framework for mpc of systems with stochastic uncertainty. Model predictive control mpc has become a dominant advanced control framework that has made a tremendous impact on both the academic and industrial control communities. Two competing versions for robust and stochastic model predictive control of. Pdf advanced textbooks in control and signal processing. Model predictive control describes the development of tractable algorithms. Model predictive control mpc, as an advanced control strategy, does not specify a special strategy but rather includes a wide range of control methods which utilize.
Classical, robust, and stochastic bookshelf abstract. An overview and perspectives for future research, ieee control systems, vol. Conditions for stochastic convergence and robust constraints ful. Model predictive control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. Classical, robust and stochastic basil kouvaritakis, mark cannon.