Distributionally Robust Model Predictive Control With Output Feedback
Bin Li, Tao Guan, Li Dai, Guang‐Ren Duan
Abstract
An output feedback stochastic model predictive control (SMPC) is proposed in this paper for a class of stochastic linear discrete-time systems, in which the uncertainties from external disturbance, measurement noise and initial state estimation error are all considered. Particularly, the support sets of the uncertainties are unbounded and the distributions are not exactly known. Based on distributionally robust optimization (DRO), a deterministic convex reformulation is derived for handling chance constraints. Recursive feasibility and convergence of the algorithm are proven. A numerical example is provided to demonstrate the effectiveness of the proposed method.
Topics & Concepts
Model predictive controlConvergence (economics)Control theory (sociology)Mathematical optimizationConvex optimizationComputer scienceNoise (video)Regular polygonRobust controlRobustness (evolution)Linear systemMathematicsControl (management)Control systemEngineeringArtificial intelligenceElectrical engineeringImage (mathematics)Economic growthChemistryGeometryEconomicsGeneMathematical analysisBiochemistryAdvanced Control Systems OptimizationFault Detection and Control SystemsControl Systems and Identification