Intelligent Tuning of Fuzzy-\(\mathcal{L}_1\) Adaptive Controller for Uncertain Nonlinear MIMO Systems Using Multi-Objective Particle Swarm Optimization
Henry A. Akinrinde *
LNG, Canada.
Hashim A. Hashim
Carleton University, Ottawa, Ontario, Canada.
Babajide O. Ayinde
EchoNous Inc., Redmond, Washington, U.S.A.
Sami El-Ferik
Department of System Engineering, King Fahd University of Petroleum and Minerals Dhahran, 31261, Saudi Arabia.
Mohamed A. Abido
Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia.
Emmanuel O. Akande
Lagos State University, Lagos, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
This paper proposes an efficient approach for tuning 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 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.
Keywords: Fuzzy logic control, multi-objective particle swarm optimization, fuzzy- adaptive controller, pareto front, filter tuning, twin rotor MIMO system