Integrated Adaptive Control and Reference Governors for Constrained Systems With State-Dependent Uncertainties

• Published in: IEEE transactions on automatic control Vol. 69; no. 5; pp. 3158 - 3173
• Main Authors: Zhao, Pan, Kolmanovsky, Ilya, Hovakimyan, Naira
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; constrained control; Constraints; Electron tubes; Flight control; Governors; Linear systems; safety-critical control; Transient analysis; Transient performance; Tuning; Uncertain systems; uncertainties; Uncertainty
• ISSN: 0018-9286; 1558-2523
• DOI: 10.1109/TAC.2023.3339499

This article presents an adaptive reference governor (RG) framework for a linear system with matched nonlinear uncertainties that can depend on both time and states, subject to both state and input constraints. The proposed framework leverages an <inline-formula><tex-math notation="LaTeX">{\mathcal {L}_{1}}</tex-math></inline-formula> adaptive controller (<inline-formula><tex-math notation="LaTeX">{\mathcal {L}_{1}}</tex-math></inline-formula>AC) that compensates for the uncertainties, and provides guaranteed transient performance in terms of uniform bounds on the error between actual states and inputs and those of a nominal (i.e., uncertainty-free) system. The uniform performance bounds provided by the <inline-formula><tex-math notation="LaTeX">{\mathcal {L}_{1}}</tex-math></inline-formula>AC are used to tighten the prespecified state and control constraints. A reference governor is then designed for the nominal system using the tightened constraints, which guarantees robust constraint satisfaction. Moreover, the conservatism introduced by constraint tightening can be systematically reduced by tuning some parameters within the <inline-formula><tex-math notation="LaTeX">{\mathcal {L}_{1}}</tex-math></inline-formula>AC. Compared with existing solutions, the proposed adaptive RG framework can potentially yield reduced conservativeness for constraint enforcement and improved tracking performance due to the inherent uncertainty compensation mechanism. Simulation results for a flight control example illustrate the efficacy of the proposed framework.