Self-tuning PID controller for activated sludge system

In this paper, a self-tuning Proportional-Integral-Derivative (PID) controller is applied to a multivariable sludge process model. The activated sludge process model is obtained using prediction error method (PEM) with best fits of higher than 80%. The obtained model is then reduced with two model reduction techniques, i.e. Moore's balanced model reduction and Enn's frequency weighted model reduction technique. For control purposes, PI and PID controllers are implemented heuristically. Therefore, to optimize these controllers, particle swarm optimization (PSO) technique is utilized as optimization algorithm in order to tune the PID parameters. From the results obtained, it is observed that the self-tuned PI controller yields a best result for the activated sludge process with a faster settling time and less percentage overshoot.