Optimal Tuning of Fuzzy Feedback filter for L1 Adaptive Controller Using Multi-Objective Particle Swarm Optimization for Uncertain Nonlinear MIMO Systems

TL;DR

This paper introduces a multi-objective particle swarm optimization method to tune fuzzy feedback filters in L1 adaptive controllers for MIMO systems, enhancing performance and stability.

Contribution

It presents a novel fuzzy-based L1 feedback filter design tuned with MOPSO for uncertain nonlinear MIMO systems, improving parameter tuning and system robustness.

Findings

Effective filter tuning improves system stability.

Simulation on twin rotor MIMO system validates approach.

Enhanced robustness and performance in uncertain conditions.

Abstract

This paper proposes an efficient approach for tuning L1 feedback filter of adaptive controller for multi-input multi-output (MIMO) systems. The feedback filter provides performance that trades off fast closed loop dynamics, robustness margin, and control signal range. Thus appropriate tuning of the filter's parameters is crucial to achieve optimal performance. For MIMO systems, the parameters tuning is challenging and requires a multi-objective performance indices to avoid instability. This paper proposes a fuzzy-based L1 feedback filter design tuned with multi-objective particle swarm optimization (MOPSO) to remove these bottlenecks. MOPSO guarantees the appropriate selection of the fuzzy membership functions. The proposed approach is validated using twin rotor MIMO system and simulation results demonstrate the efficacy of here proposed while preserving the system stabilizability.