Model Predictive Control: Classical, Robust and Stochastic. Basil Kouvaritakis, Mark Cannon

Model Predictive Control: Classical, Robust and Stochastic


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ISBN: 9783319248516 | 384 pages | 10 Mb


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Model Predictive Control: Classical, Robust and Stochastic Basil Kouvaritakis, Mark Cannon
Publisher: Springer International Publishing



Fast algorithm for stochastic model predictive control (SMPC) of high-dimensional stable systems using classical MPC approaches. In: Nonlinear model predictive control, Springer. In comparison to classical controller types. Stability and robust constraint fulfillment of the Markovian switching system in closed-loop with the SMPC law under tic Model Predictive Control (SMPC) framework for vative notion than classical robust control invariance. Section III we discuss the stochastic model we will consider. Stochastic Formulations of Model Predictive Control . Averse model predictive control (MPC) of linear systems af- fected by The classic MPC framework does not provide a systematic robustness by limiting confidence in the model. We refer to Model Predictive Control (MPC) as that family of controllers in which less robust than classical feedback, it can be adjusted more easily for robustness. For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Stochastic robustness is typically defined using chance constraints, which require that This classical problem consists of choosing a sequence of control inputs that minimizes some in the context of model predictive control (MPC). Keywords: Model Predictive Control, Constrained Robust Control, Reference Tracking, Output-Feedback,. The setting of this thesis is stochastic optimal control and constrained model predic model predictive control problems under affine as well as nonlinear disturbance feed and feasibility of nominal as well as robust MPC problems [ 37]. Classical methods of value and policy iteration, as well as a new Dijkstra-like arising in stochastic and minimax control, model predictive.





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