A simple control law analogous to the linear generalized minimum variance (GMV) control is presented for general unknown nonlinear dynamic processes. With this control law, the iterative search of the control input, which is often encountered in nonlinear control, can be eliminated, resulting in an efficient computation for real-time implementation. The implementation of this control law requires two key quantities to be calculated: the input-output sensitivity function and the quasi-one-step-ahead predictive output. The selection of a diagonal recurrent neural network (DRNN) as the process identifier allows a direct estimation of these two quantities, resulting in the proposed control law to be implemented in a straightforward manner. Both simulation and experiment are given to emonstrate the effectiveness of the proposed control algorithm.