Comparison of Different Artificial Intelligence Techniques for Optimal Tuning of PID Controller for a Continuous Stirred Tank Reactor (CSTR)

Abstract

In this study, comparison of various artificial intelligence (AI) techniques has been done for tuning optimized PID parameters in a continuous stirred tank reactor (CSTR) process. In our proposed work, we have found that the tuning should have minimum value for rise time, peak time, settling time etc. to be effective to design a CSTR system by optimal PID tuning. The simulation results of the proposed work reveal that the tuning that gives satisfactory performance in terms of minimum value of rise time, peak time, settling time, etc. and is the effective AI technique to design a CSTR system.

Keywords: PID controller, genetic algorithm (GA), CSTR, optimal tuning, particle swarm optimization (PSO), ant colony optimization (ACO)