Design and implementation of an adaptive PID controller using single neuron learning algorithm

In this paper, an adaptive single neuron-based PID controller for DC motor systems in vibratory stress relief equipment is proposed. Three inputs of the single neuron, which accord respectively with the proportion, integration and derivative of the feedback error, are associated with variable weights. The weight training algorithm is based on a combination of Delta and Hebbian learning rules. A motor speed control system with the WZ-86A DC motor was simulated using Matlab and Simulink, and further implemented on a microcomputer platform. Experiment results show that this system has satisfactory static and dynamical performances with strong robustness.