Data-driven synthesis of Robust Invariant Sets and Controllers

Abstract

This paper 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.

References in corpus (5)

• Data-Driven Model Predictive Control with Stability and Robustness Guarantees
• Robust data-driven state-feedback design
• A One-step Approach to Computing a Polytopic Robust Positively Invariant Set
• Controller design for robust invariance from noisy data
• Constrained Controller and Observer Design by Inverse Optimality

Cited by in corpus (4)

• Computation of Input Disturbance Sets for Constrained Output Reachability
• Data-driven invariant set for nonlinear systems with application to command governors
• Computation of safe disturbance sets using implicit RPI sets
• Learning Quasi-LPV Models and Robust Control Invariant Sets with Reduced Conservativeness