Knowledge-based ant colony optimization method to design fuzzy proportional integral derivative controllers

Abstract

In this paper, we propose an ant colony optimization (ACO)-based method for the design of the fuzzy proportional integral derivative (FPID) controllers for both single input single output (SISO) systems and multiple input and multiple output (MIMO) systems. Specifically, the method is used such as to minimize or maximize a cost function which quantifies the overall system performance in response to desired inputs. Without loss of generality the sum of squared error is used. The proposed method has the faculty of introducing the available knowledge about the system at hand. In order to show its efficiency, we have applied the method with tree dynamical systems; an inverted pendulum, a mini helicopter and a quadrotor.The simulation results demonstrate the efficiency of the method.