Nmodel predictive control classical robust and stochastic pdf

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. Robust and stochastic model predictive control are wellestablish paradigms to accommodate parameter uncertainty 15. In stochastic model predictive control wt is a random process, a sequence of independent, identically distributed random variables taking values in a set w. Classical, robust, and stochastic bookshelf article in ieee control systems 366. Model predictive control for stochastic systems by randomized algorithms by ivo batina. 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. Stochastic optimal control uncertain dynamical system. Model predictive control for linear systems with interval and. Two competing versions for robust and stochastic model predictive control of. Model predictive control describes the development of tractable algorithms. The starting point is classical predictive control and the appropriate.

Asymmetric distributional information in robust valueatrisk optimization, management science, 543, 573585. 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. 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. Model predictive control mpc has established itself as a. Considering the way on how both disturbances and uncertainties are modelled, robust mpc is divided into deterministic mpc dmpc and stochastic mpc smpc. In the thesis, two different model predictive control mpc strategies are investi gated for linear systems with uncertainty in the presence of constraints. The control law is obtained through the solution of a.

Chapter1 introductiontononlinearmodel predictivecontroland. Robust multistage nonlinear model predictive control. For the first time, a textbook that brings together classical predictive control with treatment of uptodate robust and stochastic techniques. Stochastic model predictive control based on gaussian. Introduction stochastic model predictive control smpc accounts for model uncertainties and disturbances based on their statistical description. It leads to nonconservative robust control of the plant because it. Nonlinear stochastic model predictive control for systems under general disturbances. Stochastic nonlinear model predictive control with e cient.

Reinforcement learning versus model predictive control. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closedloop stability and performance. The inclusion of robustness in model predictive control mpc is a wellknown research. 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. In robust model predictive control it is assumed that the disturbance w takes values in the compact set w. Sample trajectory cost histogram simple lower bound. 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.

Product size response to step changes of the bond work index of ore the result of simulation of product size control by neuralfuzzy based predictive control nfbpc is shown in fig. Modelpredictivecontrolclassicalrobustandstochastic. Lecture 17 stochastic model predictive control duration. Stochastic model predictive control of constrained linear. View essay modelpredictivecontrolclassicalrobustandstochastic. Model predictive control for stochastic systems by randomized. Pdf advanced textbooks in control and signal processing model. 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. A model predictive control strategy for distribution.

Model predictive control mpc is a control strategy that has been used successfully in numerous and diverse application areas. Model predictive control mpc is an advanced method of process control that is used to control a process while satisfying a set of constraints. Classical, robust and stochastic mpc are the main topics of this book. The robustness and stability analysis of model predictive. The system uncertainties are expressed by the following assumptions. 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. The performance objective of a model predictive control algorithm determines the optimality, stability and convergence properties of the closed loop control law. Stochastic model predictive control ali mesbah, ilya kolmanovsky and stefano di cairano i. This thesis presents multistage nonlinear model predictive control multistage nmpc as a promising nonconservative robust nmpc control scheme, which is applicable in realtime. 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. Both families of methods are based on the formulation of the control problem as a discretetime optimal control problem. A robust optimization perspective to stochastic models. The approach is based on the representation of the evolution of the uncertainty by a scenario tree. Robust model predictive control for nonlinear discretetime.

Stochastic model predictive control to solve the constrained control problem, a stochastic mpc algorithm is considered. Setpoints optimization and predictive control for grinding. Robust nonlinear model predictive control of batch processes. Scenariobased model predictive control of stochastic. The robustness and stability analysis of model predictive control. Model predictive control college of engineering uc santa barbara. In this section we consider how to generalize the quadratic cost typically employed in linear optimal control problems to account for stochastic model uncertainty. Closely related, modelbased design of experiments provides a principled. On stochastic model predictive control with bounded control. Cannon, mark and a great selection of similar new, used and collectible books available now at great prices. Robust approximation to multiperiod inventory management, under 3rd revision in operations research. Openloop optimization strategies robust model predictive control with additive uncertainty. Model predictive control classical, robust and stochastic basil.

A complete solution manual more than 300 pages is available for course instructors. Competing methods for robust and stochastic mpc sciencedirect. Assume that at time 10 for this case 1 and the state vector,0. Model predictive control classical, robust and stochastic. Bordons textbook, the technique of model predictive control or mpc has been. Model predictive control control theory mathematical. In the direct numerical optimal control literature, hicks and ray 1971. 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. In stochastic model predictive control wt is a random process, a sequence of independent, identically distributed random. Stochastic nmpc, randomized nmpc, uncertain batch polymerization reactor 1. 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. Classical, robust and stochastic advanced textbooks in control and signal processing 9783319248516 by kouvaritakis, basil. It has been in use in the process industries in chemical plants and oil refineries since the 1980s.

A constrainttightening approach to nonlinear stochastic model. Model predictive control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. Stochastic programming applied to model predictive control. Introduction model predictive control mpc is a powerful control. 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. The approach is based on repeatedly solving a stochastic optimal control problem over a. Stochastic nonlinear model predictive control of an uncertain. 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. 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. Stochastic model predictive control smpc provides a probabilistic framework for mpc of systems with stochastic uncertainty. Stochastic model predictive control how does it work. The control and analysis approaches are applied to a simulated batch crystallization process with a realistic uncertainty description. Introduction classical model predictive control robust model predictive control with additive uncertainty.

Ee364b convex optimization ii stanford engineering everywhere. Stochastic model predictive control of constrained linear systems with additive uncertainty lalo magni, daniele pala university of pavia, italy lalo. Model predictive control basil kouvaritakis, mark cannon bok. 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.