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Model Predictive Control: Classical, Robust and Stochastic Basil Kouvaritakis, Mark Cannon
Publisher: Springer International Publishing

Keywords: Model Predictive Control, stochastic disturbances, inequality constraints. Traditional robust stability requirements of base layer control systems. "This substantially limits the performance of the classical control techniques. Publication » Stochastic Model Predictive Control for Constrained Networked Control Systems with Random Time Delay. 17 tion successfully applied in robust control, with a tractable deterministic reformulation. 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? Minimax MPC and stochastic risk-sensitive control. Mizing model predictive controller, economic MPC, to ad- compression refrigeration systems is that the classical In a stochastic formula- and robust. Robust model predictive control of stable linear systems. Control constrained systems is model predictive control (MPC). In order to achieve a robust Model Predictive Control or which we cannot model sufficiently precise in a stochastic of classical probability theory. 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. 3 Model Predictive Control for Building Climate Control. The issue of stochastic stability of the closed-loop is considered. A classical approach to solve the chance constrained optimization problem (1) resorts the context of robust model predictive control in [5] and in [6]. Fast algorithm for stochastic model predictive control (SMPC) of high-dimensional stable systems using classical MPC approaches. Quadratic programming is a classical. Multivariable feedback design: Concepts for a classical/modern synthesis. 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). In this paper, we focus on Stochastic Model Predictive Control (SMPC) based on a combination of randomized and robust optimization.