Adaptive Neuron PID Control of Buck Type AC Chopper Voltage Regulator

The PID controller has been used as the effective control method. The proper adjustment of the gains is of great importance to obtain the desired performance of the controller. However, it is necessary to find suitable PID gains automatically. This paper presents the modeling and control of Buck chopper type AC voltage regulator while the system is operating under uncertainty and nonlinearity. The PID gains are tuned using improved Heb learning algorithm with single neuron network architecture, which reduce the computational complexity and adjust the controller gains in online way. The developed controller in this work, offer inherent advantages over conventional PID controller, namely: improvement of the adjusting time, output voltage overshoot and control system robustness. These advantages make the proposed method have better dynamic performance. The simulation results verify the validity and robustness of the method used in the ac voltage regulator system.