Continuous-Time Output Feedback Adaptive Control for Stabilization and Tracking with Experimental Results

TL;DR

This paper introduces a continuous-time output feedback adaptive control method that uses particle swarm optimization for hyper-parameter tuning, validated through numerical and experimental results on various systems, demonstrating its effectiveness for model-free control.

Contribution

It proposes a novel continuous-time adaptive control approach with automated hyper-parameter tuning, validated both numerically and experimentally.

Findings

Effective in stabilization and tracking tasks

Validated on double integrator and bicopter systems

Demonstrates robustness in model-free control scenarios

Abstract

This paper presents a continuous-time output feedback adaptive control technique for stabilization and tracking control problems. The adaptive controller is motivated by the classical discrete-time retrospective cost adaptive control algorithm. The particle swarm optimization framework automates the adaptive algorithm's hyper-parameter tuning. The proposed controller is numerically validated in the tracking problems of a double integrator and a bicopter system and is experimentally validated in an attitude stabilization problem. Numerical and experimental results show that the proposed controller is an effective technique for model-free output feedback control.