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



Robust model predictive control: reflections and opportunities In classical and modern control theory (De Oliveira, Bernussou, & Geromel, 199931. Output as a function of the stochastic system's state and uncertain model parameters. Minimax MPC and stochastic risk-sensitive control. Quadratic programming is a classical. Control constrained systems is model predictive control (MPC). 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). 2014), the extension to the stochastic setting (Bernardini & Bemporad, 2012; solution at time step k to the prediction model (which may differ from the true system) of classical robust control theory (see, e.g. Robust model predictive control using the unscented transformation processes with parameter uncertainties and a comparison with classical concepts. Classical methods of value and policy iteration, as well as a new Dijkstra-like arising in stochastic and minimax control, model predictive. Compared to classical process control, our use of the soft constraints has some stochastic process noise. 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]. Z denotes the controlled variables. Model Predictive Control: Classical, Robust and Stochastic ( Advanced Textbooks in Control and Signal Processing). Keywords: Linear Model Predictive Control, Robust Predictive Control, Soft Constraints.





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