Many-Objective Adaptive Fuzzy With Sliding Mode Control for a Class of Switching Power Converters Using Global Optimization

Abstract

Control of the power converters in renewable energy systems for stability and efficiency poses a technical challenge due to the intermittency of energy produced and inherent nonlinear dynamics. This paper presents a parametric optimization framework amid the synthesis of an adaptive fuzzy with sliding mode controller for a class of switching power converters suited for renewable energy systems. Four performance metrics essential to the practical needs are suggested. The potential design parameters of the controller are determined, and their influences on the performance metrics are studied and validated. A many-objective optimization problem is formulated accordingly, and a computational platform based on MATLAB/Simulink environment is established to solve the problem. Two multi-objective global search algorithms, i.e., particle swarm and bat optimization, are employed to obtain a set of Pareto optimal controllers, which noticeably enhance the performance metrics of the control system. An experimental platform with dSPACE controller board is utilized to further justify the simulation results. With those optimal controllers, the experimental results also demonstrate improvement of the performance.

Keywords

• Adaptive fuzzy control
• bat optimization
• many-objective optimization
• particle swarm optimization
• phase-shift pulse width modulation full bridge DC-DC converter
• sliding mode control