Imperialist competitive algorithm–based adaptive fuzzy control of a pneumatic actuator

Abstract

This article presents an investigation into the application of a constrained imperialist competitive algorithm with a new penalty function to optimize an adaptive fuzzy proportional–integral–derivative controller for a pneumatic actuator. The integral absolute error and the maximum overshoot of the control system are considered as the cost functions. The constrained imperialist competitive algorithm–based optimization scheme is thus conducted to obtain the best structure of the fuzzy proportional–integral–derivative controller involving optimum shape and location of membership functions and suitable value of scale factors. Then, a simulation study based on the identified model of the pneumatic actuator and three control approaches namely conventional proportional–integral–derivative control, genetic algorithm–based adaptive fuzzy proportional–integral–derivative control and the proposed constrained imperialist competitive algorithm–based adaptive fuzzy proportional–integral–derivative control is carried out to evaluate the performance of the proposed controllers. Finally, an experimental rig is developed to verify the simulation outcomes. It is found that the constrained imperialist competitive algorithm–based fuzzy controller converges faster to an optimum solution compared to the genetic algorithm method. Besides, the superiority of the proposed constrained imperialist competitive algorithm–based design over other controllers is demonstrated.