Nonlinear PID controller parameter optimization using Enhanced Genetic Algorithm for nonlinear control system

Abstract

In this brief, Enhanced Genetic Algorithm (EGA) based proportional–integral-derivative (PID) controller is presented for control of nonlinear dynamic process. In EGA, the crossover and elite kids are optimized using Ant colony Optimization (ACO) algorithm to improve convergence characteristics and optimization capabilities of traditional Genetic Algorithm (GA).The proposed algorithm is implemented for closed loop control of Continuous Stirred Tank Reactor (CSTR) process. The performance of the proposed EGA based PID is validated through the simulation results of nonlinear process by comparing them with the conventional counterpart. The integral performance criterion viz., ISE, IAE and ITAE of the EGA implemented CSTR system revealed a reduction of ISE equal to 1.5704e-4 at 50-150 sampling interval compared to conventional GA based CSTR. The results show that, EGA based nonlinear PID is more suitable for servo and regulatory operations