Robust Adaptive Control of Linear Parameter-Varying Systems with Unmatched Uncertainties

TL;DR

This paper introduces a robust adaptive control framework for LPV systems that manages both matched and unmatched uncertainties, ensuring desired dynamic scheduling and performance bounds, validated through aircraft simulations.

Contribution

It proposes a novel control architecture combining L1 adaptive control with peak-to-peak gain minimization for unmatched uncertainties in LPV systems.

Findings

Effective handling of unmatched uncertainties demonstrated.

Performance bounds derived for transient and steady-state behavior.

Validated with extensive F-16 aircraft simulations.

Abstract

In controlling systems with large operating envelopes, it is often necessary to adjust the desired dynamics according to operating conditions. This paper presents a robust adaptive control architecture for linear parameter-varying (LPV) systems that allows for the desired dynamics to be systematically scheduled, while being able to handle a broad class of uncertainties, both matched and unmatched, which can depend on both time and states. The proposed controller adopts an L1 adaptive control architecture for designing the adaptive control law and peak-to-peak gain (PPG) minimization for designing the robust control law to mitigate the effect of unmatched uncertainties. Leveraging the PPG bound of an LPV system, we derive transient and steady-state performance bounds in terms of the input and output signals of the actual closed-loop system as compared to the same signals of a nominal system. The efficacy of the proposed method is validated by extensive simulations using the short-period dynamics of an F-16 aircraft operating in a large envelope.