Shaoguang Zhang's research while affiliated with Beihang University (BUAA) and other places

The inaccuracy and delay of speed feedback cause a vibration problem when the permanent magnet synchronous motor runs at low speed. As the low-speed smoothness is a key point of many applications of the permanent magnet synchronous motor, this article solves this problem by adding a real-time adjusted extended Kalman filter to low-precision-sensor vector control. In the mathematical model, the estimated speed of the extended kalman filter is significantly impacted by the deviations of the resistance and magnetic flux rather than other parameters. Thus, under id = 0 vector control, a Rs-ψf-identifier is designed to calculate the values of the resistance and flux simultaneously with a small compensating id. This algorithm runs on the platform in real time. Finally, the experiment results validate that when the velocity reaches as low as 1 r/min, the proposed method eliminates the vibration problem in the low-precision-sensor permanent magnet synchronous motor.

Fish are very efficient swimmers. In this paper, we studied a two degree-of-freedom (DOF) propeller that mimic fish caudal fin like locomotion. Kinematics modelling and hydrodynamic CFD analyses of the two DOF propellers were conducted. According to the CFD simulation, we show that negative power was generated within the flapping cycle, and wake flow at different instant was demonstrated. Based on the dynamic model, we compared the thrust efficiency under different stiffness control method. The results show that the thrust efficiency was enhanced under moderate stiffness control strategy.

Robot manipulators should comply with the environment when working with humans. However, compliance control is a difficult problem for robots and thus it was investigated in the present study. We propose a virtual musculoskeletal control model from the perspective of bionics to facilitate the compliance of the robotic manipulator. The musculoskeletal system of the human forearm is simplified as a closed-loop control system with three parts: the central nervous system, muscles, and spindle. A mathematical model is deduced and integrated with the dynamic model of the robot manipulator. The control model also comprises three parts: the first part compensates for the Coriolis force and gravity; the second part provides stiffness to regulate the deviation; and the third part imitates the feedback from the spindle to comply with the environment. A fuzzy controller is designed based on the muscle and spindle model to obtain spindle-like feedback. Our simulation results demonstrate that the spindle-like fuzzy algorithm can adapt to the environmental constraints by imitating the function of the neural muscular system. This virtual musculoskeletal methodology allows accurate path control, but it also ensures the compliance of the robotic manipulator. These characteristics are helpful for allowing robot manipulators to cooperate with humans.

When calculating the speed from the position of permanent magnet synchronous motor (PMSM), the accuracy and real-time are limited by the precision of the sensor. This problem causes crawling and jitter at very-low speed. Using the angle from the position sensor, an extended Kalman filter (EKF) designed in dq-coordinate is presented to solve this problem. The usage of position sensor simplifies the model and improves the accuracy of speed estimation. Specially, a closed loop optimal (CLO) method is devised to overcome the difficulty to adjust the parameters of the EKF. The EKF is the feedback link of speed control, CLO method is derived from the perspective of the speed step response to optimize the measurement covariance matrix and the system covariance matrix of EKF. Simulation and experimental results, comparing the low-speed performance of the EKF and sensor feedback methods, prove the effectiveness of the method to adjust the parameters of EKF and the advantages in eliminating the low speed jitter.