A stable self-learning PID control based on the artificial immune algorithm

A new stable artificial immune self-tuning PID control scheme is proposed. The model is adapted by artificial immune clustering algorithm to learn plant dynamic change, while the PID control parameters are adapted by the Lyapunov method to minimize a cost function. Therefore, the model output is guaranteed to converge to the desired trajectory asymptotically, and the plant output also tracks the desired trajectory due to model adaptation. The merits of the proposed controllers are illustrated by considering a model of the induction motor control system and a 2 times 2 multivariable controlled plant model.