Simulink Simulation of Single Neuron PID and Smith Predictive Control Based on the s-Function

Aiming at nonlinearity and large dead-time of object to enhance dynamic quality of closed-loop system, this paper applies single neuron PID controller based on supervised Hebb learning algorithm, combined with correction of actual weighting coefficient, utilizes adaptive and self-learning ability of single neuron, tunes weights of controller and compensate for delay time, in order to improve its dynamic quality of closed-loop system. The simulation result shows that the method given in this paper can achieve good control characteristic and eliminate the impact on dynamic quality of system caused by delay time.