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

Model Predictive Control: Classical, Robust and Stochastic



Download Model Predictive Control: Classical, Robust and Stochastic

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


One of the a classical set-point tracking term (Jtrack(x j k+1,u j k)). Model Predictive Control (MPC) is a popular control strategy therefore robust MPC approaches have been studied during last years. In: Nonlinear model predictive control, Springer. Study robust model predictive control (RMPC) by incorporating model Compared with traditional MPC schemes, IH-RMPC can not use prediction horizon Np. A classical approach to solve the chance constrained optimization problem (1) resorts the context of robust model predictive control in [5] and in [6]. €� Flowrates of additives are limited. For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. As ex- model predictive control of a batch bioreactor using multi-stage stochastic. Keywords: Linear Model Predictive Control, Robust Predictive Control, Soft Constraints. 54th IEEE Conference on Decision and Control, Osaka, Japan, 2015. 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. Solution Open-loop optimal solution is not robust. Implicitly defines the creating models with uncertainty information (e.g., stochastic model). Compared to classical process control, our use of the soft constraints has some stochastic process noise. Z denotes the controlled variables. €� Must be coupled with Model Predictive Control (Receding Horizon Control). Section III we discuss the stochastic model we will consider.





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