Application of neural networks and state space averaging to a DC/DC PWM converter in sliding mode operation

A novel output feedback neural controller is presented for the implementation of sliding mode control of DC/DC converters. The controller, which consists of a multilayer perceptron, has been trained so as to be robust for large variations of system parameters and state variables. Fast dynamic behavior is the other main advantage of the proposed controller, which allows realization of all the beneficial features of sliding mode control technique. Other advantages of the controller are simplicity and low cost. Computer simulations are carried out to investigate the effectiveness of the controller in voltage regulation for a relatively complex topology such as a Cuk converter. Simulation results confirm the excellent performance of the control system in response to large signal variations. In order to verify the simulation results, a controller prototype is designed and built using analog components. The controller is applied to regulate the output voltage of a Cuk converter. The results confirm the simulation achievements