Model reference adaptive control for state and input constrained linear systems

TL;DR

This paper develops adaptive control strategies for uncertain linear systems with state and input constraints, providing easily verifiable conditions using reference modification and barrier Lyapunov methods.

Contribution

It introduces a novel adaptive control framework that simultaneously handles state and input constraints with verifiable online conditions.

Findings

Derived adaptive controllers satisfying constraints

Provided verifiable conditions for control and reference signals

Enhanced safety and reliability in constrained systems

Abstract

State and input constraints are ubiquitous in all engineering systems. In this article, we derive adaptive controllers for uncertain linear systems under pre-specified state and input constraints. Several modifications of the model reference adaptive control (MRAC) framework have been proposed to address input constraints in uncertain linear systems. Considering the infeasibility of arbitrary reference trajectories, reference modification has been implemented in the case of input constraints in literature. The resulting conditions on the reference and input signals are difficult to verify online. Similar results on state and input constraints together have also been proposed, albeit resulting in more complex and unverifiable conditions on the control. The primary objective of this article is therefore to account for state and input constraints in uncertain linear systems by providing easily verifiable conditions on the control and reference. A combination of reference modification and barrier Lyapunov methods in adaptive control are employed to arrive at these results.