Self-tuning PID controller designs for multivariable systems

In this paper, a self-tuning decentralized PID controller is developed to multivariable and nonlinear systems, using just-in-time learning (JITL) technique. From the online process data, JITL provides a set of linear models, by which the parameters of PID controllers are adjusted by a self-tuning algorithm derived from the Lyapunov method such that the predicted tracking error converges asymptotically. Simulation results illustrate the performances of the proposed self-tuning PID controller and a comparison with its conventional counterparts is made.