Risk-Aware Stochastic MPC for Chance-Constrained Linear Systems

Risk-Aware Stochastic MPC for Chance-Constrained Linear Systems

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This paper presents a fully risk-aware model predictive control (MPC) framework for chance-constrained discrete-time linear control systems with process noise.Conditional value-at-risk (CVaR) as a callaway mavrik sand wedge for sale popular coherent risk measure is incorporated in both the constraints and the cost function of the MPC framework.This allows the system to navigate the entire spectrum of risk assessments, from worst-case to risk-neutral scenarios, ensuring both constraint satisfaction and performance optimization in stochastic environments.The the william tuttle foundation recursive feasibility and risk-aware exponential stability of the resulting risk-aware MPC are demonstrated through rigorous theoretical analysis by considering the disturbance feedback policy parameterization.

In the end, two numerical examples are given to elucidate the efficacy of the proposed method.