Adaptive control of continuous time-varying bioprocesses using recursive kernel learning controller with polynomial form

Many bioprocesses are difficult to control due to their highly nonlinear and time-varying characteristics. To design simple and suitable controllers for these processes, a nonlinear predictive controller using sparse kernel learning with a polynomial kernel form is designed. First, the nonlinear time-varying processes can be identified using the recursive kernel learning method. The online kernel identification model can be efficiently updated, with nodes increasing and decreasing, via recursive learning algorithms. Consequently, the proposed polynomial kernel learning-based controller can restrict its complexity, and trace the time-varying characteristics of a nonlinear process adaptively to achieve better performance. The obtained results on a continuous bioreactor with time-varying parameters show that the proposed controller is superior to the traditional proportional-integral-derivative (PID) controller and other kernel controllers with an offline model without online updating.