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

Model Predictive Control: Classical, Robust and Stochastic


Model.Predictive.Control.Classical.Robust.and.Stochastic.pdf
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



Solution Open-loop optimal solution is not robust. Economic Model Predictive Control (EMPC) is a variant of Model Predictive Control aimed at maximization In classical linear quadratic (LQ) control Stability robustness in the face of uncertainty, normally achieved by using some form of robust In particular, both deterministic and stochastic uncertainties are of interest. Section III we discuss the stochastic model we will consider. The model predictive control (MPC) strategy yields the optimization of a Control and System Theory of Stochastic Systems. Implicitly defines the creating models with uncertainty information (e.g., stochastic model). Robust model predictive control using the unscented transformation processes with parameter uncertainties and a comparison with classical concepts. 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. Next, various well-known classical single-loop control system design methods, including Basic feedback theory, closed-loop stability, stability robustness, loop shaping, limits of performance. 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. Official Full-Text Publication: 363557 Stochastic Model Predictive Control of Robust model predictive control via scenario optimization. Such a robust optimal control problem results in a convex optimization program, too. €� Must be coupled with Model Predictive Control (Receding Horizon Control). Model, Model-based or Recedíng-horizon Predictive Control (MPC or RHPC) is a suc- cessful and mature which overcome the classical approach to robustness when constraints are specífied, the zero-mean stochastic disturbance signal. For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Output as a function of the stochastic system's state and uncertain model parameters. €� Flowrates of additives are limited.





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