Online adaptation of kernel learning adaptive predictive controller
核学习自适应预测控制器的在线更新方法

To design controllers for nonlinear processes, a sparse kernel learning adaptive predictive controller with an analytical form is extended to the updated form using the selective recursive kernel learning method. The online kernel learning model can be efficiently updated with node increment and decrement via recursive learning algorithms. Consequently, the proposed kernel controller can restrict its complexity and adaptively trace the time-varying characteristics of a process to achieve better performance. Simulation of the proposed kernel controller for a nonlinear time-varying process is performed. In comparing with the traditional PID controller and the related kernel controller without online updating, this controller exhibits more satisfactory adaptation and robustness.