Soft Computing Based PID Controller Tuning and Application to the Pulp and Paper Industry

Abstract

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 and Bacterial foraging optimization. The proposed algorithm is used to tune the PI parameters and its performance has been compared with the conventional methods like Ziegler Nichols and Cohen Coon method. The results obtained reflect that use of heuristic algorithm 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 paper discusses in detail, the Soft computing technique and its implementation in PI tuning for a controller of a Pulp and paper industry process. Compared to other conventional PI tuning methods, the result shows that better performance can be achieved with the soft computing based tuning method. The ability of the designed controller in terms, of tracking set point is also compared and simulation results are shown.

Keywords

• Genetic algorithm
• Particle swarm optimization
• PID controller and bacterial foraging