Papers by Author: Quan Wang

Abstract: This paper presents the inversion power-supply control system with the software of dual SPWM modulation system and voltage closed loop PI control strategy and the hardware foundation of DSP TMS320LF2407A. Aiming at the requirements of rapidity of system, it adopts the feedforward and amplitude control strategy on aspect of control strategy to reduce the dynamic response error. Aiming at the requirements of stability and stationarity, it takes the segmentation intelligent PI control strategy. In order to avoid the system oscillation caused by the input direct current voltage fluctuation, it designs the voltage sluggish loop in the software. It has significant effects in improving the inversion wave form, anti-interference and anti-bias magnet, ensuring the stability of system, reducing the harmonic wave and undesired sound, and improving the wave form quality and the adaptation to load, etc.

Abstract: Flow equalization is one of the key technologies in the parallel control of inversion module. This paper presents the multi-inversion module parallel flow equalization system with TMS320LF2407A as the main control chip, which adopts the control strategy of “synchronous control of seizure and coincidence”, gains the data communication in the multi-module parallel connection and the multi-inversion module parallel flow equalization control with CAN controller embedded in TMS320LF2407A. It can be seen from the experimental data and experimental wave that it can effectively restrain the ring current and achieve the equipartition of load in the condition of resistive load and inductive load or capacitive load to achieve the data communication among the parallel inversion modules.

Abstract: Many researchers have indicated that standard genetic algorithm suffers from the dilemma---premature or non-convergence. Most researchers focused on finding better search strategies, and designing various new heuristic methods. It seemed effective. From another view, we can transform search space with a samestate-mapping. A special genetic algorithm applied to the new search space would achieve better performance. Thus, we present a new genetic algorithm based on optimal solution orientation. In this paper, a new genetic algorithm based on optimum solution orientation is presented. The algorithm is divided into "optimum solution orientation" phase and "highly accurately searching in local domain of global optimal solution" phase. Theoretical analysis and experiments indicate that OSOGA can find the "optimal" sub domain effectively. Cooperating with local search algorithm, OSOGA can achieve highly precision solution with limited computing resources.

Abstract: A self-constructing fuzzy neural network (SCFNN) based on reinforcement learning is proposed in this study. In the SCFNN, structure and parameter learning are implemented simultaneously. Structure learning is based on uniform division of the input space and distribution of membership function. The structure and membership parameters are organized as real value chromosomes, and the chromosomes are trained by the reinforcement learning based on genetic algorithm. This paper uses Matlab/Simulink to establish simulation platform and several simulations are provided to demonstrate the effectiveness of the proposed SCFNN control stratagem with the implementation of AC motor speed drive. The simulation results show that the AC drive system with SCFNN has good anti-disturbance performance while the load change randomly.