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

Z denotes the controlled variables. € Must be coupled with Model Predictive Control (Receding Horizon Control). Solution Open-loop optimal solution is not robust. A stochastic model predictive control (SMPC) design approach is proposed to Conditions for stochastic convergence and robust constraints is shown on a numerical example and compared to traditional MPC schemes. € Flowrates of additives are limited. 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? 17 tion successfully applied in robust control, with a tractable deterministic reformulation. Keywords: Linear Model Predictive Control, Robust Predictive Control, Soft Constraints. 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). 3 Model Predictive Control for Building Climate Control. "This substantially limits the performance of the classical control techniques. 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.