Abstract: An adaptive single-neuron control system based on neuron-fuzzy model is proposed in this paper. Firstly, the nonlinear process model is identified by input-output points. Then the single-neuron controller, which is adjusted using the Lyapunov method, is considered so that the setpoint can be rapidly tracked by the output of the system. Theory analysis and simulation results show that the proposed singleneuron controller mimics the conventional PID controller. Consequently, it possesses simple structure and can be easily operated . Moveover, this adaptive single-neuron controller is better than conventional PID controller, and the parameters of the controller are on-line adjusted.