Application of Ant Colony Optimization in Tuning a PID Controller to a Conical Tank

Abstract

In this paper, PID controller with ant colony optimization is developed for the conical tank. Ant Colony Optimization (ACO) is a recently developed meta-heuristic approach for solving hard combinational optimization problems. Each individual ant can find a solution or at least part of a solution to the optimization problem on its own, but only when many ants work together they can find the optimal solution. Since the optimal solution can only be found through the global cooperation of all the ants in a colony, it is an emergent result of such cooperation. The results of PID controller with ant colony optimization provides a remarkable improvement in tracking a given set point, when compared with the Ziegler Nichols closed loop tuning and feed forward plus feedback controller tuning methods.