Data-Driven Safe Output Regulation of Strict-Feedback Linear Systems with Input Delay

TL;DR

This paper presents a data-driven control framework for strict-feedback linear systems with unknown parameters and input delay, using Koopman operator theory, system identification, and control barrier functions to ensure safety and convergence.

Contribution

It introduces a novel combination of Koopman-based system identification, batch least-squares, and control barrier functions for safe output regulation of systems with unknown dynamics and delays.

Findings

Finite-time identification of unknown system parameters.

Exponential convergence of output states to reference trajectories.

Successful application to safe vehicle platooning.

Abstract

This paper develops a data-driven safe control framework for linear systems possessing a known strict-feedback structure, but with most plant parameters, external disturbances, and input delay being unknown. By leveraging Koopman operator theory, we utilize Krylov dynamic mode decomposition (DMD) to extract the system dynamics from measured data, enabling the reconstruction of the system and disturbance matrices. Concurrently, the batch least-squares identification (BaLSI) method is employed to identify other unknown parameters in the input channel. Using control barrier functions (CBFs) and backstepping, we first develop a full-state safe controller. Based on this, we build an output-feedback controller by performing system identification using only the output data and actuation signals as well as constructing an observer to estimate the unmeasured plant states. The proposed approach achieves: 1) finite-time identification of a substantial set of unknown system quantities, and 2) exponential convergence of the output state (the state furthest from the control input) to a reference trajectory while rigorously ensuring safety constraints. The effectiveness of the proposed method is demonstrated through a safe vehicle platooning application.