OPTIMIZATION OF PID CONTROLLERS OF CASCADED SYSTEMS USING HYBRID METAHEURISTIC ALGORITHM MODEL

Abstract

This thesis presents a novel methodology for optimizing a Cascaded Proportional Integral-Derivative (PID) controller for a flow-level control system using a hybrid metaheuristic algorithm approach. The proposed model integrates the Genetic Algorithm (GA) and Ant Lion Optimizer (ALO) combining the strengths of both the standalone algorithms. System used is flow-level control system which is a Cascaded system, known for its effectiveness in improving the disturbance rejection capabilities and dynamic response of control systems. In this work, the inner loop regulates the faster flow dynamics, while the outer loop addresses the slower level dynamics. The hybrid GA-ALO algorithm is designed to capitalize on the global search capabilities of GA and the exploitation efficiency of ALO, thus overcoming limitations like premature convergence and slow optimization often associated with single algorithms. The PID parameters are optimized through a weighted objective function considering rise time, settling time, overshoot, and integral absolute error (IAE). This research not only provides an effective control strategy for flow-level systems but also highlights the potential of hybrid metaheuristic algorithms in complex control optimization problems.