Self-Tuning fuzzy controller for sun-tracker system using Gray Wolf Optimization (GWO) technique

Abstract

The demand of electric power consumption is increasing very rapidly worldwide and to fulfill this requirement, solar energy is one of the most viable solution as renewable energy source. Photovoltaic (PV) cell based sun-tracker system (STS) produces maximum current when sunlight vertically incident on its surface. Hence, there is a need of optimized continuous axis position control of STS to achieve maximum output current. This task can be done on the basis of the fuzzy control system. Usually, in the traditional fuzzy control system (FCS), tuning of designed fuzzy parameter is done by trial and error method. However, this type of FCS parameter tuning approach may or may not give optimal solution. Thus, in presented work, an optimal tuning technique with Takagi, Sugeno and Kang (TSK) fuzzy controller (TFC) using Gray Wolf Optimization (GWO) for STS has been proposed. In order to validate the proposed work, different objective functions have been employed to carry out fuzzy controller parameter optimization. A comparative analysis has been performed on the basis of three parameters: settling time, maximum-overshoot and optimal fuzzy parameter on different constrain set. The results obtained with the GWO optimization algorithm were also compared with other popular population algorithms, i.e. Whale Optimization Technique (WOT) and Particle Swarm Optimization (PSO) algorithms.