A novel control framework based on sparse kernel learning one-step-ahead predictive control (SKL-OPC) was presented for the general unknown nonlinear processesThe polynomial kernel function was adopted to derive a simple control strategy as a natural extension of SKLOPCThe manipulated input can be separated from the control performance index due to the special structure of polynomial kernelConsequently

the control problem was resolved by solving the roots of an odddegree polynomial equationThe proposed control strategy did not require the nonlinear optimization technique

which resulted in a small computation scale and made it very suitable for realtime controlApplication of the proposed approach to a highly nonlinear continuous stirred tank reactor indicated its validity and showed superior performance

compared to other methods.