Tuning of a PID Controller using Soft Computing Methodologies Applied to Moisture Control in Paper Machine

Proportional – Integral – Derivative control schemes continue to provide the simplest and effective solutions to most of the control engineering applications today. However PID controller is poorly tuned in practice with most of the tuning done manually which is difficult and time consuming. This research comes up with a soft computing approach involving Genetic Algorithm, Evolutionary Programming, Particle Swarm Optimization, Bacterial Foraging Optimization and heuristic algorithm bacterial foraging combine with particle swarm optimization. The proposed soft computing is used to tune the PID parameters and its performance has been compared with the conventional method Ziegler Nichols. The results obtained reflect that use of soft computing based controller improves the performance of process in terms of time domain specifications, set point tracking, and regulatory changes and also provides an optimum stability. This research addresses comparison of tuning of the PID controller using soft computing techniques on moisture control system in paper machine (Machine Direction).