Grey Wolf Optimizer-Based Approach to the Tuning of Pi-Fuzzy Controllers with a Reduced Process Parametric Sensitivity

This paper suggests the use of Grey Wolf Optimizer (GWO) algorithms to tune the parameters of Takagi-Sugeno proportional-integral-fuzzy controllers (PI-FCs) for a class of nonlinear servo systems. The servo systems are modelled by second order dynamics plus a saturation and dead zone static nonlinearity. The GWO algorithms solve the optimization problems that minimize discrete-time objective functions expressed as the weighted sum of the squared control error and of the squared output sensitivity function in order to achieve the parametric sensitivity reduction. The output sensitivity function is derived from the sensitivity model with respect to the modification of the process gain, and fuzzy control systems with a reduced process gain sensitivity are offered. Three parameters of Takagi-Sugeno PI-FCs are obtained by a new cost-effective tuning approach. Experimental results related to the angular position control of a laboratory servo system validate the tuning approach.