The invention relates to an improved single-neuron PID tuning method based on a discrete system. When a control system is in the dynamic state, a single-neuron PID controller with the self-adaptive proportionality coefficient lambda is adopted, and the single-neuron proportionality coefficient is completely controlled by the input-output error e(k), so that parameters of the single-neuron PID are completely and automatically tuned by the system, and manual evaluation is not required; the single-neuron PID is poor in steady state effect, so that after the system is in the steady state, the conventional PID with the self-adaptive integral grain Ki is adopted to improve the steady-state performance, and the integral grain parameter Ki is also completely controlled by the input-output error e(k) of a controlled object. According to the method, all the parameters are automatically tuned, the method is high in robustness, and the control effect cannot be degraded along with aging and damage to the controlled object; the parameters can be adjusted automatically during interference, so that the effect caused by the interference on the control system is reduced.