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Articles by Bing-Gang Cao
Total Records ( 4 ) for Bing-Gang Cao
  Zhi-Feng Bai , Shu-Xin Li and Bing-Gang Cao
  The state space model and the design of H robust controller for regenerative braking of an EV(electric vehicle) driven by permanent magnet DC motor were presented. The controller was developed to make a good combination between regenerative braking and mechanical friction braking system and to minimize the effect of disturbance, such as the variation of initial speed and driving mode. The experimental results with different initial speed and driving modes show that energy saving and good combination between regenerative braking and mechanical friction braking are synchronously available by H controller and the H controller is prior to the traditional PID controller in both steady-state tracking error and response speed.
  Jian Wang , Long-yun Kang and Bing-Gang Cao
  This study proposed a new energy control strategy for a distributed power generation system based on renewable. A mathematical model of the system was built based on a vector-controlled induction machine driving a flywheel. The BP Neural network control method was designed in the system in order to regulate DC Bus voltage, which is the object of the system. The experimental results on a wind simulator and flywheel based system verified that proposed energy complementary control can satisfactorily regulate the power of the storage unit to store and release energy and thus to maintain a steady DC voltage from the distributed power generation system.
  Jun-Yi Cao , Bing-Gang Cao and Zhong Liu
  An estimation algorithm for driving resistance load of Electric Vehicles (EV) has been proposed in this study. Driving resistance load is an important factor in the motion control of EV. It is a large and time varying disturbance, so it is difficult to measure and unable to be compensated only by the design of robust controller. Therefore, an approach using an unknown input observer is introduced to estimate driving resistance load. The effectiveness of the observer-based methods is proved through the XJTUEV (Xi`an Jiaotong University EV)-1 simulation model in different road conditions.
  Liu Zhong , Xiao-Geng Liang , Bing-Gang Cao and Jun-Yi cao
  A novel robust adaptive backstepping control approach is presented to design a pitch controller for missiles employing blended aerodynamic control and Reaction-jet Control System (RCS). The main features of this study are that a certain type of control Lyapunov function is obtained by function reconstruction to decrease the control-input fluctuation caused by mismatched controller parameters, optimal backstepping parameter set and Lyapunov function choice are gotten by Genetic Algorithm (GA). So both system stability and dynamic performance are guaranteed. Since the model of a missile with RCS is inaccurate with uncertain effect of RCS and aerodynamic parameters, a Fuzzy Cerebellar Model Articulation Controller (FCMAC) neural network is used to guarantee controller robustness, thus the bounds of model uncertainties are not needed. Finally, simulation results demonstrate the efficiency and advantages of the proposed method.
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