Dynamic trajectory-tracking control method of robotic transcranial magnetic stimulation with end-effector gravity compensation based on force sensors

Lin, Zijia; Wang, Xin; Yang, Jian; Zhang, Qingpei; ZongJie, Lu

doi:10.1108/ir-03-2018-0051

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…Finally, the modified cartesian interpolation points are converted into joint interpolation points to drive the robot. According to Lin et al (2018), the logarithmic adaptive function has good rapidity and accuracy. To make the PD controller adapt to environments with various stiffness, the proportional and differential gains of the PD constant force controller are set to change against the estimated stiffness; this makes the robot produce more indentation depth in environments with low stiffness and produce less indentation depth in environments with high stiffness, so that a constant force will be controlled.…”

Section: Constant Force Controlmentioning

confidence: 99%

Robotic direct grinding for unknown workpiece contour based on adaptive constant force control and human–robot collaboration

Zhao

Xiao

Liu

et al. 2022

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Purpose In customized production such as plate workpiece grinding, because of the diversity of the workpiece shapes and the positional/orientational clamping errors, great efforts are taken to repeatedly calibrate and program the robots. To change this situation, the purpose of this study is to propose a method of robotic direct grinding for unknown workpiece contour based on adaptive constant force control and human–robot collaboration. Design/methodology/approach First, an adaptive constant force controller based on stiffness estimation is proposed, which can distinguish the contact of the human hand and the unknown workpiece contour. Second, a normal vector search algorithm is developed to calculate the normal vector of the unknown workpiece contour in real-time. Finally, the force and position are controlled in the calculated normal and tangential directions to realize the direct grinding. Findings The method considers the disturbance of the tangential grinding force and the friction, so the robot can track and grind the workpiece contour simultaneously. The experiments prove that the method can ensure the force error and the normal vector calculating error within 0.3 N and 4°. This human–robot collaboration pattern improves the convenience of the grinding process. Research limitations/implications The proposed method realizes constant force grinding of unknown workpiece contour in real-time and ensures the grinding consistency. In addition, combined with human–robot collaboration, the method saves the time spent in repeated calibration and programming. Originality/value Compared with other related research, this method has better accuracy and anti-disturbance capability of force control and normal vector calculation during the actual grinding process.

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Section: Constant Force Controlmentioning

confidence: 99%

Robotic direct grinding for unknown workpiece contour based on adaptive constant force control and human–robot collaboration

Zhao

Xiao

Liu

et al. 2022

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“…In this method, the attitude angle feedback and gravity compensation are independent of the robot. ZeCai Lin et al studied the force perception problem of robots under dynamic conditions, used BP neural network to predict the influence of load gravity through the robot joint angle, and further used analytical methods to calculate the influence of inertial force/moment to realize the force sensing on the end of the robot [20]. Kamal Mohy el Dine et al used recurrent neural network (RNN), using the robot end pose and IMU feedback data as input to predict the force of the robot end [21].…”

Section: Figure 1 Schematic Diagram Of the Force Acting On The Loadmentioning

confidence: 99%

Research on Force Sensing and Zero Gravity Motion Simulation Technology of Industrial Robot

Meng

Zhang

et al. 2021

Preprint

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Towards the zero-gravity simulation test requirements of spacecraft for on-orbit service missions, the zero-gravity motion simulation technology based on industrial robot was studied. In order to realize the accurate force sensing of load on robot, the BP neural network was used to establish the force prediction model. The model uses the robot pose, acceleration, angular velocity, and angular acceleration as input layer parameters, and uses the data from force/torque sensor on robot wrist as the output layer parameter. In order to realize the accurate force perception in whole working space of the robot, the orthogonal experiment design method was adopted to determine the path points of the robot for sample data collection. Based on the established force prediction model, the accurate force sensing of the end load of the robot under moving conditions is realized. In experiment, for the load with 100kg mass on robot, the maximum error of force sensing is 39.8N, and the root mean square error of force sensing is 9.3N. The maximum error of torque sensing is 18.7Nm, and the root mean square error of torque sensing is 4.4Nm. Furthermore, according to the real-time force sensing results of the robot load, the motion speed of the load in zero-gravity state is calculated by dynamics theory. Then the robot is driven to execute the corresponding motion of the load, and the zero gravity motion simulation of the load is realized.

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“…It is difficult to realize the above requirements only relying on the position control of the robot (Hong et al, 2020). So it is necessary to add end effector to realize constant force grinding (Lin et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Force tracking control of grinding end effector based on backstepping + PID

Dai

et al. 2021

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Purpose This study aims to realize the constant force grinding of automobile wheel hub. Design/methodology/approach A force control strategy of backstepping + proportion integration differentiation (PID) is proposed. The grinding end effector is installed on the flange of the robot. The robot controls the position and posture of the grinding end actuator and the grinding end actuator controls the grinding force output. First, the modeling and analysis of the grinding end effector are carried out, and then the backstepping + PID method is adopted to control the grinding end effector to track the expected grinding force. Finally, the feasibility of the proposed method is verified by simulation and experiment. Findings The simulation and experimental results show that the backstepping + PID strategy can track the expected force quickly, and improve the dynamic response performance of the system and the quality of grinding and polishing of automobile wheel hub. Research limitations/implications The mathematical model is based on the pneumatic system and ideal gas, and ignores the influence of friction in the working process of the cylinder, so the mathematical model proposed in this study has certain limitations. A new control strategy is proposed, which is not only used to control the grinding force of automobile wheels, but also promotes the development of industrial control. Social implications The automatic constant force grinding of automobile wheel hub is realized, and the manpower is liberated. Originality/value First, the modeling and analysis of the grinding end effector are carried out, and then the backstepping + PID method is adopted to control the grinding end effector to track the expected grinding force. The nonlinear model of the system is controlled by backstepping method, and in the process, the linear system composed of errors is obtained, and then the linear system is controlled by PID to realize the combination of backstepping and PID control.

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Dynamic trajectory-tracking control method of robotic transcranial magnetic stimulation with end-effector gravity compensation based on force sensors

Cited by 7 publications

References 20 publications

Robotic direct grinding for unknown workpiece contour based on adaptive constant force control and human–robot collaboration

Robotic direct grinding for unknown workpiece contour based on adaptive constant force control and human–robot collaboration

Research on Force Sensing and Zero Gravity Motion Simulation Technology of Industrial Robot

Force tracking control of grinding end effector based on backstepping + PID

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