Improved control strategy on buck-boost converter fed DC motor

This paper presents comparison of the performance of neural network controller with that of conventional open loop and closed loop controllers for buck-boost converter fed dc motor based on voltage control method. It describes the use of neural networks in a control loop applied to ac-dc buck-boost converter fed dc motor. The proposed technique makes use of the learning capability of neural networks to implement an auto-adaptive control structure. Such capability allows the network to learn the dynamic behavior of the buck-boost converter fed DC Motor. The performance of the proposed method is investigated using the MATLAB simulation models of buck-boost converter fed dc motor. Neural network controller based on pulse area modulation is built. Closed loop controller provides better dynamic control when compared to other controllers. Comparisons between the proposed Neural Network controller and conventional controller responses are provided through dynamic simulation.