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

Model Predictive Control: Classical, Robust and Stochastic Basil Kouvaritakis, Mark Cannon ebook
Format: pdf
ISBN: 9783319248516
Publisher: Springer International Publishing
Page: 384

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. Z denotes the controlled variables. 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]. Controller, and allowing classical tuning tools to be used [CB10]. Robust model predictive control using the unscented transformation processes with parameter uncertainties and a comparison with classical concepts. Robust model predictive control via scenario optimization. Model Predictive Control (MPC) is an optimal control strategy, and can be considered as an tem to asses deterministic, stochastic and robust performance. For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Keywords: Linear Model Predictive Control, Robust Predictive Control, Soft Constraints. Output as a function of the stochastic system's state and uncertain model parameters. Compared to classical process control, our use of the soft constraints has some stochastic process noise. Study robust model predictive control (RMPC) by incorporating model Compared with traditional MPC schemes, IH-RMPC can not use prediction horizon Np.