This book offers readers a thorough and rigorous introduction to nonlinear model predictive control NMPC for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC. Nonlinear Model Predictive Control. Usually dispatched within 3 to 5 business days. Usually dispatched within 3 to 5 business days. During the past decade model predictive control MPC, also referred to as receding horizon control or moving horizon control, has become the preferred control strategy for quite a number of industrial processes. Nonlinear Model Predictive Control is a thorough and rigorous introduction to NMPC for discrete-time and sampled-data systems. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. Nonlinear Model Predictive Control is a thorough and rigorous introduction to NMPC for discrete-time and sampled-data systems. NMPC is interpreted as an approximation of infinite-horizon optimal. Jan 01, 1994 · The differences between optimal control and model predictive control are illustrated with a stochastic control example. Nominal stability is proved for a class of nonlinear plants. The major topics of current research in the field are summarized. K e y Words, systems. predictive control, optimal control, nonlinear programming, nonlinear control 1.

Institute for Systems Theory in Engineering, University of Stuttgart 70550 Stuttgart, Germany Abstract─While linear model predictive control is popular since the 70s of the past century, only since the 90s there is a steadily increasing interest from control theoreticians as well as control practitioners in nonlinear model predictive control. Basically, linear MPC and nonlinear MPC NMPC are distinguished see also Model Based Predictive Control for Linear Systems and Model Based Predictive Control. Linear MPC refers to a family of MPC schemes in which linear models are used to predict the system dynamics, even though the dynamics of the closed-loop system might. simple, reasonably general, nonlinear system theory could be developed. Hand in hand with this viewpoint was the feeling that many of the approaches useful for linear systems ought to be extensible to the nonlinear theory. This is a key point if the theory is. Jul 04, 2017 · [27] Dang T. V., Tran T. and Ling K., “ Numerical Algorithms for Quadratic Programming in Model Predictive Control—An Overview,” Proceedings of the ISSAT International Conference on Modelling of Complex Systems and Environment, Danang, Vietnam, 2015.

The concept of optimal control, and in particular its practical implementation in terms of Nonlinear Model Predictive Control NMPC is an attractive alternative since the complexity of the control design and speciﬁcation increases moder- ately with the size and complexity of the system. ~~Open problems include, as in linear model predictive control, estimation, adaptation and robustness but, in addition, there is a daunting challenge: the solution, online, of non-convex optimal control problems. This paper reviews the convergence of methodologies for Nonlinear Model Predictive Control, assesses the challenges arising from uncertainty and from non-convexity of the optimal control problem.~~ Nonlinear Model Predictive Control. During the past decade model predictive control MPC, also referred to as receding horizon control or moving horizon control, has become the preferred control strategy for quite a number of industrial processes. T. I. Fossen, Guidance and Control of Ocean Vehicles. John Wiley & Sons, 1994. Google Scholar; F. Allgower and A. Zheng, Nonlinear Model Predictive Control -- Progress in Systems and Control Theory, vol. 26. pages 461--471, Birkhauser Verlag, Basl-Boston-Berlin, 2000. Google Scholar.

Nonlinear Model Predictive Control, or NMPC, is a variant of model predictive control MPC that is characterized by the use of nonlinear system models in the prediction. As in linear MPC, NMPC requires the iterative solution of optimal control problems on a finite prediction horizon. Nonlinear model predictive control henceforth abbreviated as NMPC is an opti- mization based method for the feedback control of nonlinear systems. Its primary applications arestabilizationandtrackingproblems, which we brieﬂy introduce in order to describe the basic idea of model predictive control. Abstract: Implementation of model predictive control MPC or nonlinear systems requires the online solution of a nonconvex, constrained nonlinear optimisation problem. Computational delay and loss of optimality arise in the optimisation procedures. The paper presents a practical MPC scheme for nonlinear systems with guaranteed asymptotic stability.

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