Optimizing an Adaptive Fuzzy Logic Controller of a 3-DOF Helicopter with a Modified PSO Algorithm

TL;DR

This paper presents a modified particle swarm optimization algorithm to enhance the tuning of an adaptive fuzzy logic controller for a nonlinear 3-DOF helicopter, demonstrating improved accuracy and convergence in simulations.

Contribution

It introduces a novel MPSO algorithm that outperforms standard PSO and other metaheuristics in optimizing a complex helicopter controller.

Findings

MPSO achieves better accuracy than standard PSO.

MPSO converges faster in controller optimization.

The method is effective under uncertainties and disturbances.

Abstract

This paper investigates the controller optimization for a helicopter system with three degrees of freedom (3-DOF). To control the system, we combined fuzzy logic with adaptive control theory. The system is extensively nonlinear and highly sensitive to the controller's parameters, making it a real challenge to study these parameters' effect on the controller's performance. Using metaheuristic algorithms for determining these parameters is a promising solution. This paper proposes using a modified particle swarm optimization (MPSO) algorithm to optimize the controller. The algorithm shows a high ability to perform the global search and find a reasonable search space. The algorithm modifies the search space of each particle based on its fitness function value and substitutes weak particles for new ones. These modifications have led to better accuracy and convergence rate. We prove the efficiency of the MPSO algorithm by comparing it with the standard PSO and six other well-known metaheuristic algorithms when optimizing the adaptive fuzzy logic controller of the 3-DOF helicopter. The proposed method's effectiveness is shown through computer simulations while the system is subject to uncertainties and disturbance. We demonstrate the method's superiority by comparing the results when the MPSO and the standard PSO optimize the controller.