Data-Driven Synthesis of Robust Invariant Sets and Controllers
Sampath Kumar Mulagaleti, Alberto Bemporad, Mario Zanon
Abstract
This letter presents a method to identify an uncertain linear time-invariant (LTI) prediction model for tube-based Robust Model Predictive Control (RMPC). The uncertain model is determined from a given state-input dataset by formulating and solving a Semidefinite Programming problem (SDP), that also determines a static linear feedback gain and corresponding invariant sets satisfying the inclusions required to guarantee recursive feasibility and stability of the RMPC scheme, while minimizing an identification criterion. As demonstrated through an example, the proposed concurrent approach provides less conservative invariant sets than a sequential approach.
Topics & Concepts
Semidefinite programmingControl theory (sociology)Invariant (physics)Model predictive controlLTI system theoryRobust controlMathematicsLinear programmingComputer scienceScheme (mathematics)Mathematical optimizationLinear systemControl (management)Control systemArtificial intelligenceEngineeringMathematical analysisMathematical physicsElectrical engineeringAdvanced Control Systems OptimizationFault Detection and Control SystemsControl Systems and Identification