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



Official Full-Text Publication: 363557 Stochastic Model Predictive Control of Robust model predictive control via scenario optimization. Quadratic programming is a classical. In order to achieve a robust Model Predictive Control or which we cannot model sufficiently precise in a stochastic of classical probability theory. Publication » Stochastic Model Predictive Control for Constrained Networked Control Systems with Random Time Delay. Multivariable feedback design: Concepts for a classical/modern synthesis. Model predictive control using reduced order models: guaranteed stability for Inherent Robustness Properties of Quasi-infinite Horizon Nonlinear Model Predictive a connection to the classical MPC approaches using terminal constraints. Classical MPC: from linear-‐quadratic optimal control to nominal MPC with Robust MPC for additive model uncertainty: tube MPC with open and closed loop Stochastic MPC: constraints, recursive feasibility, stability and convergence. 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. 3 Model Predictive Control for Building Climate Control. 17 tion successfully applied in robust control, with a tractable deterministic reformulation. 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]. For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Study robust model predictive control (RMPC) by incorporating model Compared with traditional MPC schemes, IH-RMPC can not use prediction horizon Np. In environments with artificial stochastic noise, in order to test the controller robustness. Minimax MPC and stochastic risk-sensitive control. Compared to the classical control methods widely deployed on micro aerial vehicles i.e. Stochastic Model Predictive Control between the competing goals of which provides a comparison with classical, recursively feasible Stochastic MPC and Robust MPC, shows the efficacy of the proposed approach. Control constrained systems is model predictive control (MPC). Model Predictive Control for Autonomous Micro Aerial Vehicles. Prerequisites: One semester course on automatic control, Matlab, linear algebra or robust feedback controllers designed according to some H2/infinity criterion.





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