Design and optimization of a joint torque sensor with small crosstalk error for robots

Bing, Fu; Cai, Ganwei

doi:10.1016/j.measurement.2022.111629

Cited by 7 publications

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can be seen from the previous chapters that the leg system of the hydraulic quadruped robot is a robust coupling system, which must be decoupled (Fu and Cai, 2022). In the course of robot joint decoupling control, system uncertainty, parameter change, environmental interference and various nonlinear factors should be fully considered.…”

Section: Designs Neural Network Model Reference Decoupling Controller...mentioning

confidence: 99%

Research on decoupling control of single leg joints of hydraulic quadruped robot

Gao,

Zhao,

Han

et al. 2024

RIA

View full text Add to dashboard Cite

Purpose This study proposes a predictive neural network model reference decoupling control method for the coupling problem between the leg joints of hydraulic quadruped robots, and verifies its decoupling effect.. Design/methodology/approach The machine–hydraulic cross-linking coupling is studied as the coupling behavior of the hydraulically driven quadruped robot, and the mechanical dynamics coupling force of the robot system is controlled as the disturbance force of the hydraulic system through the Jacobian matrix transformation. According to the principle of multivariable decoupling, a prediction-based neural network model reference decoupling control method is proposed; each module of the control algorithm is designed one by one, and the stability of the system is analyzed by the Lyapunov stability theorem. Findings The simulation and experimental research on the robot joint decoupling control method is carried out, and the prediction-based neural network model reference decoupling control method is compared with the decoupling control method without any decoupling control method. The results show that taking the coupling effect experiment between the hip joint and knee joint as an example, after using the predictive neural network model reference decoupling control method, the phase lag of the hip joint response line was reduced from 20.3° to 14.8°, the amplitude attenuation was reduced from 1.82% to 0.21%, the maximum error of the knee joint coupling line was reduced from 0.67 mm to 0.16 mm and the coupling effect between the hip joint and knee joint was reduced from 1.9% to 0.48%, achieving good decoupling. Originality/value The prediction-based neural network model reference decoupling control method proposed in this paper can use the neural network model to predict the next output of the system according to the input and output. Finally, the weights of the neural network are corrected online according to the predicted output and the given reference output, so that the optimization index of the neural network decoupling controller is extremely small, and the purpose of decoupling control is achieved.

show abstract

Section: Designs Neural Network Model Reference Decoupling Controller...mentioning

confidence: 99%

Research on decoupling control of single leg joints of hydraulic quadruped robot

Gao,

Zhao,

Han

et al. 2024

RIA

View full text Add to dashboard Cite

show abstract

A six-axis force and torque sensor consisting of compliant mechanisms and full-bridge strain gauges

Wang,

Chi,

Lin

et al. 2024

Measurement

View full text Add to dashboard Cite

Design, optimization, and crosstalk analysis for robot joint torque sensor of shear beam structure

Chen

Xie

Sun

et al. 2023

Review of Scientific Instruments

View full text Add to dashboard Cite

Crosstalk resistance is an important criterion for evaluating the measurement error of the Joint Torque Sensor (JTS) in actual collaborative robot application, but few research literature studies on the crosstalk resistance of shear beam-type JTS have been found. This paper proposes a mechanical structure of one shear beam sensor and determines its strain gauge working area. Multi-objective optimization equations are established with three major performance indicators of sensitivity, stiffness, and crosstalk resistance. Optimal processing and manufacturing structure parameters are obtained by employing both the response surface method based on the central composite design experimental principle and the multi-objective genetic algorithm. By simulating and experimenting, the optimized sensor is verified and has the following indices: overload resistance 300% F.S., torsional stiffness 503.44 KN m/rad, bending stiffness 142.56 KN m/rad, range 0–±200 N m, sensitivity 25.71 mV/N m, linearity 0.1999%, repeatability error 0.062%, hysteresis error 0.493%, measurement error less than 0.5% F.S. under Fx (392.4 N) or Fz (600 N) crosstalk load, and measurement error less than 1% F.S. under My (25 N m) moment crosstalk. The proposed sensor possesses good crosstalk resistance and especially axial crosstalk resistance and has good overall performance to meet well the engineering requirements.

show abstract

Design and optimization of a joint torque sensor with small crosstalk error for robots

Cited by 7 publications

References 29 publications

Research on decoupling control of single leg joints of hydraulic quadruped robot

Research on decoupling control of single leg joints of hydraulic quadruped robot

A six-axis force and torque sensor consisting of compliant mechanisms and full-bridge strain gauges

Design, optimization, and crosstalk analysis for robot joint torque sensor of shear beam structure

Contact Info

Product

Resources

About