Torque estimation for robotic joint with harmonic drive transmission based on system dynamic characteristics

In the applications of joint control and robot movement,the joint torque estimation has been treated as an effective technique and widely used.Researches are made to analyze the kinematic and compliance model of the robot joint with harmonic drive to acquire high precision torque output.Through analyzing the structures of the harmonic drive and experiment apparatus,a scheme of the proposed joint torque estimation method based on both the dynamic characteristics and unscented Kalman filter(UKF)is designed and built.Based on research and scheme,torque estimation methods in view of only harmonic drive compliance model and compliance model with the Kalman filter are simulated as guidance and reference to promote the research on the torque estimation technique.Finally,a promoted torque estimation method depending on both harmonic drive compliance model and UKF is designed,and simulation results compared with the measurements of a commercial torque sensor,have verified the effectiveness of the proposed method.

Electromagnetic Torque Estimation of a Three-Phase Induction Motor for Setting the Feedrate of a Milling Machine

In this paper, it presents a project of a fuzzy controller and a neural estimator to control a coordinate table powered by three-phase induction motor, aiming to implement an intelligent milling system. The position/speed control is performed using vector techniques of three-phase induction machines. The estimation of the motor electromagnetic torque is used for setting the feedrate of the table. The speed control is developed using TS （Takagi-Sugeno） fuzzy logic model and electromagnetic torque estimation using neural network type LMS （least mean square） algorithm. The induction motor is powered by a frequency inverter driven by a DSP （digital signal processor）. Control strategies are implemented in DSP. Simulation results are presented for evaluating the performance of the system.

Position Sensorless Torque Estimation of Synchronous Reluctance Motors Based on Extended-Flux Model without Core-Loss Measurement

This paper proposes an extended-flux model with core-loss resistance of SynRMs （synchronous reluctance motors） and precise torque estimation without core-loss measurement and position encoder. The proposed torque estimation is useful for precise MTPA （maximum torque per ampere） control of position sensorless controlled SynRMs, which is achieved with the assistance of active and reactive powers.

A Sensor-less Surface Mounted PMSM for Electronic Speed Control in Multilevel Inverter

Recent advancements in power electronics technology evolves inverter fed electric motors.Speed signals and rotor position are essential for controlling an electric motor accurately.In this paper,the sensorless speed control of surface-mounted permanent magnet synchronous motor(SPMSM)has been attempted.SPMSM wants a digital inverter for its precise working.Hence,this study incor-poratesfifteen level inverter to the SPMSM.A sliding mode observer(SMO)based sensorless speed control scheme is projected to determine rotor spot and speed of the multilevel inverter(MLI)fed SPMSM.MLI has been operated using a multi carrier pulse width modulation(MCPWM)strategy for generation offif-teen level voltages.The simulation works are executed with MATLAB/SIMU-LINK software.The steadiness and the heftiness of the projected model have been investigated under no loaded and loaded situations of SPMSM.Furthermore,the projected method can be adapted for electric vehicles.

Kalman Filter-Based Fusion Estimation Method of Steering Feedback Torque for Steer-by-Wire Systems

Universal challenge lies in torque feedback accuracy for steer-by-wire systems,especially on uneven and low-friction road.Therefore,this paper proposes a fusion method based on Kalman filter that combines a dynamics-reconstruction method and disturbance observer-based method.The dynamics-reconstruction method is designed according to the vehicle dynamics and used as the prediction model of the Kalman filter.While the disturbance observer-based method is performed as an observer model of the Kalman filter.The performance of all three methods is comprehensively evaluated in a hardware-in-the-loop system.Experimental results show that the proposed fusion method outperforms dynamics reconstruction method and disturbance observer-based method.Specifically,compared with the dynamics-reconstruction method,the root mean square error is reduced by 36.58%at the maximum on the flat road.Compared with the disturbance observer-based method,the root mean square error is reduced by 39.11%at the maximum on different-friction and uneven road.