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

TL;DR

This paper introduces an adaptive reference governor framework that combines L1 adaptive control with constraint tightening to improve constraint satisfaction and tracking performance in systems with state-dependent uncertainties.

Contribution

It presents a novel integrated adaptive control and reference governor approach that reduces conservatism and enhances robustness for constrained systems with nonlinear uncertainties.

Findings

Reduced conservatism in constraint enforcement.

Improved tracking performance.

Effective in flight control simulation.

Abstract

This paper 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 L1 adaptive controller (L1AC) that estimates and 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 L1AC are used to tighten the pre-specified state and control constraints. A reference governor is then designed for the nominal system using the tightened constraints, and guarantees robust constraint satisfaction. Moreover, the conservatism introduced by the constraint tightening can be systematically reduced by tuning some parameters within the L1AC. Compared with existing solutions, the proposed adaptive RG framework can potentially yield less conservative results for constraint enforcement due to the removal of uncertainty propagation along a prediction horizon, 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.