Data Driven Safe Gain-Scheduling Control

TL;DR

This paper introduces a data-driven method for designing safe gain-scheduling controllers for LPV systems that guarantees safety and stability without requiring explicit system identification, using collected data directly.

Contribution

It proposes a novel data-based approach to design safe gain-scheduling controllers for LPV systems, bypassing system identification and ensuring safety through $\lambda$-contractivity.

Findings

Data-based representation enables direct safe control policy learning.

Weaker data richness needed compared to system identification.

Linear and semi-definite programs effectively design controllers.

Abstract

Data-based safe gain-scheduling controllers are presented for discrete-time linear parameter-varying systems (LPV) with polytopic models. First, $\lambda$-contractivity conditions are provided under which safety and stability of the LPV systems are unified through Minkowski functions of the safe sets. Then, to bypass the requirement to identify the system dynamics, a data-based representation of the closed-loop LPV system is provided to directly exploit collected data and construct a safe controller. It is shown that weaker data richness requirements are needed to directly learn a closed-loop safe control policy than to identify the LPV system. The closed-loop data-based representation is leveraged to directly design data-driven gain-scheduling controllers that guarantee $\lambda$-contractiveness, and, thus, invariance of the safe sets. It is also shown that the problem of designing a data-driven gain-scheduling controller for a polyhedral (ellipsoidal) safe set amounts to a linear program (a semi-definite program). A simulation example is provided to show the effectiveness of the presented approach.