Adaptive Gravitational Search Algorithm for PI-fuzzy Controller Tuning

Abstract

This paper proposes an adaptive Gravitational Search Algorithm (aGSA) focused on tuning of Takagi-Sugeno PI-fuzzy controllers (T-S PI-FCs). The algorithm adapts two depreciation laws of the gravitational constant to the iteration index, a parameter in the weighted sum of all forces exerted from the other agents to the iteration index, and the reset at each stage of agents' worst fitnesses and positions to their best values. Two fuzzy logic blocks carry out the adaptation of the ratios of exploration runs and explanation runs using the ratio between the minimum and maximum Popov sums as an input variable. A tuning method for T-S PI-FCs dedicated to a class of nonlinear servo systems with an integral component and is offered, and T-S PI-FCs with reduced process gain sensitivity are tuned. A case study and digital simulation results illustrate the optimal tuning of a T-S PI-FC for the position control of a laboratory servo system.