Kernel learning adaptive one-step-ahead predictive control for nonlinear processes

A Kernel learning adaptive one-step-ahead Predictive Control (KPC) algorithm is proposed for the general unknown nonlinear processes. The main structure of the KPC law is twofold. A one-step-ahead predictive model is first obtained by using the kernel learning (KL) identification framework. An analytical control law is then derived from Taylor linearization method, resulting in an efficient computation for on-line implementation. The convergence analysis or the KPC control strategy is presented, meanwhile a new concept of adaptive modification index is proposed to improve the tracking ability of KPC and reject the unknown disturbance. This simple KPC scheme has few parameters to be chosen and small computation scale, which make it very suitable for real-time control. Numerical Simulations compared with a well-tuned proportional-integral-derivative (PID) controller on a nonlinear chemical process show the new KPC algorithm exhibits much better performance and more satisfactory robustness to both additive noise and unknown process disturbance. (c) 2008 Curtin University of Technology and John Wiley & Sons, Ltd.