Less Conservative Adaptive Gain-scheduling Control for Continuous-time Systems with Polytopic Uncertainties

TL;DR

This paper introduces a less conservative adaptive gain-scheduling control method for continuous-time systems with polytopic uncertainties, improving flexibility and reducing conservativeness through novel uncertainty description techniques.

Contribution

It presents a new adaptive gain-scheduling control approach that minimizes conservativeness by advanced uncertainty modeling and relaxation techniques.

Findings

Reduces conservativeness compared to existing methods

Demonstrates improved control performance in numerical examples

Provides a more accurate uncertainty representation

Abstract

The synthesis of adaptive gain-scheduling controller is discussed for continuous-time linear models characterized by polytopic uncertainties. The proposed approach computes the control law assuming the parameters as uncertain and adaptively provides an estimate for the gain-scheduling implementation. Conservativeness is reduced using our recent results on describing uncertainty: i) a structural relaxation that casts the parameters as outer terms and introduces slack variables; and ii) a precise topological representation that describes the mismatch between the uncertainty and its estimate. Numerical examples illustrate a high degree of relaxation in comparison with the state-of-the-art.