The Digital and Intelligent Control of PSFB DC/DC Converter with Current-Doubler-Rectifier
|Course||Control Theory and Control Engineering|
|Keywords||digital control fuzzy control neural network control PSFB current-doubler-rectifier|
In high power fields, PSFB(phase shift full bridge) with Current-Doubler-Rectifier has a good performance, and its output ripple current is reduced, because the ripple currents of two filter inductor are partially canceled each other. In this thesis, the operation modes of the circuit are analyzed in detail, and the main components of the circuit are designed and selected. The main circuit is simulated at circuit-level by using the Simetrix/Simplis software, and the resonant inductance is optimized designed, the simulation results show that the analysis of the operation modes of the circuit is right. The small signal model of the PSFB with Current-Doubler-Rectifier is established and its transfer function is derived. And then the double-closed-loop digital PI controller of the converter is designed and is simulated by using MATLAB software. By using the TMS320F2808DSP of TI, the full digital control of PSFB with Current-Doubler-Rectifier is realized, A prototype with the output28V/18A is built, the experimental results show that the circuit realizes soft-switch and the main waveforms of the circuit are consistent with the principle waveform.Considering the shortcomings of that the parameters of the PID controller are difficult to set and can not change with the change of the controlled object characteristic once the parameters setting. In this thesis, by combing the intelligent control with the PID control, good control results are gained.First, the fuzzy control is researched. By combing the fuzzy control with the PID and using the fuzzy rules to change the three parameters of PID, the parameters can change when the load and input voltage fluctuations, in this way the controller can adapt to the different operation conditions. Then a fuzzy PID controller is designed, and the simulation is realized with the MATLAB software. After that, the fuzzy PID control is implemented in engineering by using the look-up table method. The simulation and experimental results show that the fuzzy PID control has a better performance in dynamic and static characteristics than the digitalPID control(the regulating time is shorter, the overshooting is smaller). When the scale factor (ke、kec) change, the system’s performance will be effected. In this thesis, some research is also done about this effect, and the result is given.Furthermore, by combing the BP neural network with the PID and using the self-learning ability of the BP neural network to change the parameters of PID, the cumbersome process of setting the parameters of PID is dropped. And the parameters of PID can change with the change of the characteristic of the controlled object, so the parameters of PID is dynamic adjusted. A BP neural network PID controller is designed. The PSFB converter controlled by the BP neural network PID is simulated with the MATLAB software, the simulation results show that the BP neural network PID control has a better performance in dynamic and static characteristics than the digital PID control(the regulating time is shorter, the overshooting is smaller).