Self-Tuning PID Based on Adaptive Genetic Algorithms with the Application of Activated Sludge Aeration Process

Activated sludge aeration process is a non-linear system subject to large perturbations in flow and load, together with uncertainties on the composition of the incoming wastewater. Traditional PID control lacks the ability to overcome these disturbances. In order to achieve adaptive process performances, an adaptive genetic algorithm (AGA) was proposed to tune the parameters of an activated sludge aeration PID controller. Several self-tuning algorithms, specifically Ziegler-Nichols (Z-N), internal model control (IMC) and Kappa-tau were also applied in order to tune an activated sludge aeration PID controller. Performance results of these controllers were compared by simulation with those obtained by using an adaptive genetic algorithm (AGA) to tune PID controller. By comparing, the proposed algorithm, which is used to tune the parameters of PID controller, achieves satisfactory response of a PID controller actuating over the aeration process in an activated sludge process.