Using Particle Swarm Optimization for Fuzzy Antecedent Parameter Identification in Active Suspension Control
Isabel Elena Herrera, Anthony Mandow, Alfonso J. Garcia-Cerezo · 2018
This paper addresses fuzzy parameter identification by using Particle Swarm Optimization (PSO) techniques with an application to active suspension control. In particular, the target fuzzy controller is a zero-order Takagi-Sugeno system with a standard fuzzy partition (SFP) of its antecedent variables. The major contribution of this paper with respect to previous works is that learning of the fuzzy suspension control is not limited to the scale factors of the input-output variables. Thus, the proposed approach allows optimization of SFP triangular membership functions for the antecedents with a manageable dimension of the search space. This method has been successfully applied to control a quarter-car test rig.