Robust Control of Unknown Switched Linear Systems from Noisy Data

TL;DR

This paper develops a noise-robust data-driven control method for unknown switched linear systems, ensuring stability despite noisy data and slow system switching, with practical implementation via intermittent SDP solutions.

Contribution

It introduces an auxiliary function-based switching control law that maintains stability under noisy conditions and slow switching, requiring only intermittent SDP solutions.

Findings

Guarantees input-to-state practical stability under bounded noise.

Effective in systems with slow switching dynamics.

Validated through numerical examples.

Abstract

This paper investigates the problem of data-driven stabilization for linear discrete-time switched systems with unknown switching dynamics. In the absence of noise, a data-based state feedback stabilizing controller can be obtained by solving a semi-definite program (SDP) on-the-fly, which automatically adapts to the changes of switching dynamics. However, when noise is present, the persistency of excitation condition based on the closed-loop data may be undermined, rendering the SDP infeasible. To address this issue, an auxiliary function-based switching control law is proposed, which only requires intermittent SDP solutions when its feasibility is guaranteed. By analyzing the relationship between the controller and the system switching times, it is shown that the proposed controller guarantees input-to-state practical stability (ISpS) of the closed-loop switched linear system, provided that the noise is bounded and the dynamics switches slowly enough. Two numerical examples are presented to verify the effectiveness of the proposed controller.