A Multi Objective Hybrid Algorithm to Optimize Set-point Filter based PID Controller for a Class of Systems

Abstract

This paper proposes a novel hybrid evolutionary algorithm using Particle Swarm assisted Bacterial Foraging Optimization algorithm for the closed loop automatic tuning of a set-point filter and PID controller for a class of chemical systems operating at unstable steady state. In this work, the PSO algorithm is employed in the optimization search to add the velocity parameter for the tumbling operation of the bacterial foraging algorithm, which can speed up the algorithm convergence. The need for a suitable PID controller structure for the evolutionary algorithm based search is discussed in detail. In the proposed work, the optimization process is focused to search the best possible controller parameters (Kp, Ki, Kd) and set-point filter parameter (Tf) by minimizing the multi objective performance index. The effectiveness of the proposed scheme has been confirmed through a comparative study with PSO, IPSO, BFO and the classical controller tuning methods proposed in the literature. The results show that, the proposed method provides enhanced performance in effective reference tracking with minimal ISE and IAE values. Finally the robustness of the proposed method is validated by operating the unstable systems in the presence of a measurement noise. The results testify that the PSO-BFO tuned set-point filter based PID performs well in tracking the change in reference signal even in the noisy environment.