In this paper,a sensorless control strategy of a permanent magnet synchronous machine(PMSM)based on an improved rotor flux observer(IFO)is proposed.Due to the unknown integral initial value and the high harmonics caus...In this paper,a sensorless control strategy of a permanent magnet synchronous machine(PMSM)based on an improved rotor flux observer(IFO)is proposed.Due to the unknown integral initial value and the high harmonics caused by current sampling and inverter nonlinearities,the flux linkage estimated by traditional rotor flux observer may be inaccurate.In order to address these issues,a self-adaptive band-pass filter(SABPF)is designed to eliminate the DC component and high-frequency harmonics of the estimated equivalent rotor flux linkage.Furthermore,in order to avoid that the design of PI parameter is influenced by the amplitude of equivalent rotor flux linkage,an improved phase-locked loop(IPLL)is employed to obtain the rotor speed and to normalize the estimated equivalent rotor flux linkage.In addition,angle shift caused by an SABPF is compensated to improve the accuracy of the estimated flux linkage angle.Besides,the parameter robustness of this method is analyzed in detail.Finally,simulation and experimental results demonstrate the effectiveness and parameter robustness of the proposed method.展开更多
Accurate landing detection is crucial for humanoid robots performing high dynamic motions.Unlike common methods that rely on redundant force-torque sensors and low-precision observers to estimate landing states,this p...Accurate landing detection is crucial for humanoid robots performing high dynamic motions.Unlike common methods that rely on redundant force-torque sensors and low-precision observers to estimate landing states,this paper proposes a novel landing detection method characterized by high precision and low noise,synthesizing a learning-based Improved Momentum Observer(IMO-Net)for the ankles’external torque estimation with a Gated Recurrent Unit(GRU)-based network for state judgment.Since the movement and external torque of the ankle undergo drastic changes during high dynamic motions,achieving accurate and real-time estimation presents a challenge.To address this problem,IMO-Net employs a new Improved Momentum Observer(IMO),which does not depend on acceleration data derived from second-order differentials or friction model,and significantly reduces noise effects from sensors data and robot foot wobble.Furthermore,an Elman network is utilized to accurately calculate the ankle output torque(IMO input),significantly reducing the estimation error.Finally,leveraging IMO-Net and extensive experimental data,we developed and optimized a GRU-based landing detection network through comprehensive ablation experiments.This refined network reliably determines the robot’s landing states in real-time.The effectiveness of our methods has been validated through experiments.展开更多
基金This work has been partly supported by National Natural Science Foundation of China(NSFC 51877093,51707079,and 51807075),National Key Research and Development Program of China(Project ID:YS2018YFGH000200),and Fund。
文摘In this paper,a sensorless control strategy of a permanent magnet synchronous machine(PMSM)based on an improved rotor flux observer(IFO)is proposed.Due to the unknown integral initial value and the high harmonics caused by current sampling and inverter nonlinearities,the flux linkage estimated by traditional rotor flux observer may be inaccurate.In order to address these issues,a self-adaptive band-pass filter(SABPF)is designed to eliminate the DC component and high-frequency harmonics of the estimated equivalent rotor flux linkage.Furthermore,in order to avoid that the design of PI parameter is influenced by the amplitude of equivalent rotor flux linkage,an improved phase-locked loop(IPLL)is employed to obtain the rotor speed and to normalize the estimated equivalent rotor flux linkage.In addition,angle shift caused by an SABPF is compensated to improve the accuracy of the estimated flux linkage angle.Besides,the parameter robustness of this method is analyzed in detail.Finally,simulation and experimental results demonstrate the effectiveness and parameter robustness of the proposed method.
基金supported in part by the Beijing Natural Science Foundation under Grant L243004in part by the National Natural Science Foundation of China under Grant 62073041in part by the“111”Project under Grant B08043.
文摘Accurate landing detection is crucial for humanoid robots performing high dynamic motions.Unlike common methods that rely on redundant force-torque sensors and low-precision observers to estimate landing states,this paper proposes a novel landing detection method characterized by high precision and low noise,synthesizing a learning-based Improved Momentum Observer(IMO-Net)for the ankles’external torque estimation with a Gated Recurrent Unit(GRU)-based network for state judgment.Since the movement and external torque of the ankle undergo drastic changes during high dynamic motions,achieving accurate and real-time estimation presents a challenge.To address this problem,IMO-Net employs a new Improved Momentum Observer(IMO),which does not depend on acceleration data derived from second-order differentials or friction model,and significantly reduces noise effects from sensors data and robot foot wobble.Furthermore,an Elman network is utilized to accurately calculate the ankle output torque(IMO input),significantly reducing the estimation error.Finally,leveraging IMO-Net and extensive experimental data,we developed and optimized a GRU-based landing detection network through comprehensive ablation experiments.This refined network reliably determines the robot’s landing states in real-time.The effectiveness of our methods has been validated through experiments.
