Nmodel predictive control classical robust and stochastic pdf

Model predictive control classical, robust and stochastic. Stochastic model predictive control smpc provides a probabilistic framework for mpc of systems with stochastic uncertainty. Classical, robust and stochastic advanced textbooks in control and signal processing kindle edition by kouvaritakis, basil. 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 college of engineering uc santa barbara. Robust nonlinear model predictive control of batch processes. Bordons textbook, the technique of model predictive control or mpc has been. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closedloop stability and performance. 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. Classical, robust and stochastic mpc are the main topics of this book. Model predictive control mpc is a control strategy that has been used successfully in numerous and diverse application areas. In the thesis, two different model predictive control mpc strategies are investi gated for linear systems with uncertainty in the presence of constraints. The robustness and stability analysis of model predictive control. Model predictive control for stochastic systems by randomized.

Assume that at time 10 for this case 1 and the state vector,0. Cannon, mark and a great selection of similar new, used and collectible books available now at great prices. The inclusion of robustness in model predictive control mpc is a wellknown research. It leads to nonconservative robust control of the plant because it. Stochastic model predictive control of constrained linear. Classical, robust, and stochastic bookshelf model predictive control mpc has become a dominant advanced control framework that has made a tremendous. The aim of the present entry is to discuss how the basic ideas of mpc can be extended to problems involving random model uncertainty with known probability distribution. Stochastic model predictive control smpc refers to a family of numerical optimization strategies for controlling stochastic systems subject to constraints on the states and inputs of the controlled system. Robust model predictive control for nonlinear discretetime. Lecture 17 stochastic model predictive control duration.

Introduction stochastic model predictive control smpc accounts for model uncertainties and disturbances based on their statistical description. A robust optimization perspective to stochastic models. Closely related, modelbased design of experiments provides a principled. Classical, robust and stochastic basil kouvaritakis, mark cannon for the first time, a textbook that brings together classical predictive control with treatment of uptodate robust and stochastic techniques. Stochastic nonlinear model predictive control with e cient sample approximation of chance constraints stefan streifa,b,d, matthias karlb, ali mesbahc ainstitute for automation and systems engineering, ilmenau university of technology, 98684 ilmenau, germany. Classical, robust and stochastic advanced textbooks in control and signal processing 9783319248516 by kouvaritakis, basil. The proposed robust nmpc algorithm improes the robust performance by a factor of six compared to open loop optimal control, and a factor of two. Robust multistage nonlinear model predictive control. Model predictive control classical, robust and stochastic basil. A complete solution manual more than 300 pages is available for course instructors. In stochastic model predictive control wt is a random process, a sequence of independent, identically distributed random variables taking values in a set w.

Model predictive control describes the development of tractable algorithms. Alamo abstractmany robust model predictive control mpc schemes are based on minmax optimization, that is, the future control input trajectory is chosen as the one which minimizes the performance due to the worst disturbance. Model predictive control for linear systems with interval and. Robust and stochastic model predictive control are wellestablish paradigms to accommodate parameter uncertainty 15. A constrainttightening approach to nonlinear stochastic model. The approach is based on repeatedly solving a stochastic optimal control problem over a. Scenariobased model predictive control of stochastic. Model predictive control mpc is an advanced method of process control that is used to control a process while satisfying a set of constraints. This thesis presents multistage nonlinear model predictive control multistage nmpc as a promising nonconservative robust nmpc control scheme, which is applicable in realtime. Model predictive control mpc has established itself as a. Setpoints optimization and predictive control for grinding. Stochastic programming applied to model predictive control. Stochastic optimal control uncertain dynamical system. The control and analysis approaches are applied to a simulated batch crystallization process with a realistic uncertainty description.

Nonlinear stochastic model predictive control for systems under general disturbances. Stochastic model predictive control how does it work. Stochastic nonlinear model predictive control with e cient. Modelpredictivecontrolclassicalrobustandstochastic. Model predictive control control theory mathematical. Stochastic model predictive control based on gaussian. Stochastic nonlinear model predictive control of an uncertain. For the first time, a textbook that brings together classical predictive control with treatment of uptodate robust and stochastic techniques. View essay modelpredictivecontrolclassicalrobustandstochastic. In the direct numerical optimal control literature, hicks and ray 1971. In recent years it has also been used in power system balancing models and in power electronics. Openloop optimization strategies robust model predictive control with additive uncertainty. Classical, robust and stochastic pdf description for the first time, a textbook that brings together classical predictive control with treatment of uptodate robust and stochastic techniques.

Robust and multiobjective model predictive control design for nonlinear systems and submitted in partial ful llment of the requirements for the degree of doctor of philosophy mechanical engineering complies with the regulations of this university and meets the accepted standards with respect to originality and quality. Robust approximation to multiperiod inventory management, under 3rd revision in operations research. Stochastic model predictive control ali mesbah, ilya kolmanovsky and stefano di cairano i. Stochastic model predictive control causal statefeedback control stochastic finite horizon control solution via dynamic programming independent process noise linear quadratic stochastic control certainty equivalent model predictive control stochastic mpc. Classical, robust, and stochastic bookshelf article in ieee control systems 366. From robust model predictive control to stochastic optimal. Assume prediction and control horizon are 10 and 4, calculate the component of a predictive control sequence for future output y, and the values, and data vector from the set point information. Ee364b convex optimization ii stanford engineering everywhere. Model predictive control for stochastic systems by randomized algorithms by ivo batina. Classical, robust, and stochastic bookshelf abstract. Model predictive control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. Introduction model predictive control mpc is a powerful control. The performance objective of a model predictive control algorithm determines the optimality, stability and convergence properties of the closed loop control law. Introduction classical model predictive control robust model predictive control with additive uncertainty.

The starting point is classical predictive control and the appropriate. Stochastic model predictive control to solve the constrained control problem, a stochastic mpc algorithm is considered. This paper describes a model predictive control mpc algorithm for the solution of a statefeedback robust control problem for discretetime nonlinear systems. Stochastic model predictive control of constrained linear systems with additive uncertainty lalo magni, daniele pala university of pavia, italy lalo. In stochastic model predictive control wt is a random process, a sequence of independent, identically distributed random.

Asymmetric distributional information in robust valueatrisk optimization, management science, 543, 573585. On stochastic model predictive control with bounded control inputs peter hokayem, debasish chatterjee, john lygeros abstractthis paper is concerned with the problem of model predictive control and rolling horizon control of discretetime systems subject to possibly unbounded random noise inputs, while satisfying hard bounds on the control. Stochastic programming applied to model predictive control d. Competing methods for robust and stochastic mpc sciencedirect. Convexication for model predictive control under uncertainty with reliable online computations the workshop also provides real life applications and reports on the actual transition from theory to practice. The approach is based on the representation of the evolution of the uncertainty by a scenario tree. Two competing versions for robust and stochastic model predictive control of. Sample trajectory cost histogram simple lower bound. Pdf advanced textbooks in control and signal processing model. 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. Both families of methods are based on the formulation of the control problem as a discretetime optimal control problem. Chapter1 introductiontononlinearmodel predictivecontroland. On stochastic model predictive control with bounded control.

Reinforcement learning versus model predictive control. 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. The robustness and stability analysis of model predictive. A model predictive control strategy for distribution. The system uncertainties are expressed by the following assumptions. The control law is obtained through the solution of a. 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. Considering the way on how both disturbances and uncertainties are modelled, robust mpc is divided into deterministic mpc dmpc and stochastic mpc smpc. Model predictive control basil kouvaritakis, mark cannon bok. In this section we consider how to generalize the quadratic cost typically employed in linear optimal control problems to account for stochastic model uncertainty.