Robust adaptive model predictive control with persistent excitation conditions
Xiaonan Lu, Mark Cannon
Abstract
For constrained linear systems with bounded disturbances and parametric uncertainty, we propose a robust adaptive model predictive control strategy with online parameter estimation. Constraints enforcing persistently exciting closed loop control actions are introduced for a set-membership parameter identification scheme. The algorithm requires the online solution of a convex program, satisfies constraints robustly, and ensures recursive feasibility and input-to-state stability. Almost sure convergence to the actual system parameters is demonstrated under assumptions on stabilizability, reachability, and tight disturbance bounds.
Topics & Concepts
ReachabilityControl theory (sociology)Model predictive controlParametric statisticsBounded functionConvergence (economics)Adaptive controlRobust controlStability (learning theory)Mathematical optimizationIdentification (biology)Computer scienceLinear systemMathematicsRegular polygonEstimation theorySet (abstract data type)Control (management)Control systemAlgorithmEngineeringArtificial intelligenceBotanyMachine learningGeometryEconomic growthEconomicsElectrical engineeringMathematical analysisProgramming languageBiologyStatisticsAdvanced Control Systems OptimizationControl Systems and IdentificationFault Detection and Control Systems