The control of permanent magnet synchronous motor based on position sensor not only increases the design cost, but also reduces the system reliability. The principle of Extended Kalman filter, EKF) is analyzed and studied. The EKF optimization algorithm is applied to the control of permanent magnet synchronous motor, and the rotor speed and rotor position of permanent magnet synchronous motor are predicted and estimated. Finally, based on the matlab simulation platform, the extended Kalman filter (EKF) simulation model is constructed. The simulation results show that the rotor position and rotor speed of PMSM can be accurately predicted and estimated, and the simulation model based on extended Kalman filter (EKF) algorithm is effective, which provides a solid theoretical foundation for the research of PMSM control system.
The stooping and handling exoskeleton robot integrating multi-field technologies such as mechatronics, human-computer interaction, gait analysis and intelligence is an important development direction in the field of robot research and development in recent years. The boosting efficiency is the most critical evaluation index in the overall performance of the exoskeleton robot. In this paper, by analyzing the changes of the human lumbar spine force in the state of normal bending and carrying under the state of natural motion cycle, the state of wearing the exoskeleton without assistance, and the state of wearing exoskeleton assistance, this paper establishes a bending and carrying model, completes the force analysis, and deduces the power-assisting efficiency. Compare the calculation formula, and design a wearable stooping exoskeleton robot based on this, and conduct EMG experiment data in three states to collect the changes of the user's biomechanical signals. The percentage of EMG signal amplitude of the erector spinae muscles of the waist in the state decreased by 39.3%, verifying its beneficial effect on users.
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