Velocity Estimation of Robot Manipulators: An Experimental Comparison

Liu, S. B.; Giusti, Andrea; Althoff, Matthias

doi:10.1109/ojcsys.2022.3222753

Cited by 5 publications

(2 citation statements)

References 54 publications

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“…As the tracking objective is in generally specified in terms of the angular position, this work focuses on the situation where the velocity measurements of the robotic manipulator are unavailable [23]. On the one hand, as shown in references [24,25], the information on the joint velocity is essential to construct a stabilizing controller. On the other hand, the joint velocity cannot be obtained by differentiating the angular data due to the sensor noise [26].…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural network control of robotic manipulators with input constraints and without velocity measurements

Zhang,

Zhao,

Wang

et al. 2024

IET Control Theory & Appl

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This paper addresses the trajectory tracking problem for a class of uncertain manipulator systems under the effect of external disturbances. The main challenges lie in the input constraints and the lack of measurements of joint velocities. An extend‐state‐observer is utilized to estimate the velocity signals; then, a neural‐network‐based adaptive controller is proposed to solve the problem, where a term based on the nominal model is included to enhance the tracking ability, and the effect of uncertainties and disturbances are compensated by a neural‐network term. Compared with the existing methods, the main distinctive features of the presented approach are: (i) The control law is guaranteed to be bounded by design, instead of directly bounded by a saturation function. (ii) The trade‐off between the performance and robustness of the presented controller can be easily tuned by a parameter that depends on the size of model uncertainties and external disturbances. By virtue of the Lyapunov theorem, the convergence properties of the proposed controller are rigorously proved. The performance of the controller is validated via both simulations and experiments conducted on a two‐degree‐of‐freedom robot manipulator.

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Section: Introductionmentioning

confidence: 99%

Adaptive neural network control of robotic manipulators with input constraints and without velocity measurements

Zhang,

Zhao,

Wang

et al. 2024

IET Control Theory & Appl

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show abstract

“…Marquette scholars in the United States proposed to use the least square method to process low-resolution position signals [4], which improved the continuity and stability of the signal. However, when the motor starts, it is difficult to estimate the rotor position and speed effectively using this method.…”

Section: Introductionmentioning

confidence: 99%

High-Precision Acquisition Method of Position Signal of Permanent Magnet Direct Drive Servo Motor at Low Speed

Zhang

Dong

et al. 2023

Energies

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This paper studies a method for high-precision acquisition of position signals for permanent magnet direct drive servo motors at low speed. First of all, the problem of poor position feedback accuracy and sensor feedback delay in the low-speed operation of the permanent magnet direct drive servo motor is analyzed. Secondly, through analysis and simulation, it is found that the interpolation method can play a certain role in compensating the rotor position signal. However, when the speed is close to 0, the output signal of the sensor will fluctuate in a short time, which will affect the speed control accuracy. Therefore, this paper uses the observer method to achieve high-precision acquisition of the position signal of the permanent magnet direct drive servo motor at low speed. The observer method adopts the idea of combining the system model and closed-loop control. Additionally, it makes full use of the parameter information of the motor system. The control performance of the motor can be better guaranteed through the design of the observer parameters and the accuracy of the rotor position estimation result has been greatly improved. Finally, an experimental platform for permanent magnet direct drive servo motors is built, and the rotor position signal acquisition method based on the observer method is verified to have good performance through simulation and experiments. Not only the accuracy of the rotor position estimation result is improved, but also the motor control performance is improved, realizing the stable operation of the permanent magnet direct drive servo motor at low speed.

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On the parametrization of algebraic differentiators in the light of ‘A survey on the discrete-time differentiators in closed-loop control systems: Experiments on an electro-pneumatic system’

Othmane

2024

Control Engineering Practice

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Velocity Estimation of Robot Manipulators: An Experimental Comparison

Cited by 5 publications

References 54 publications

Adaptive neural network control of robotic manipulators with input constraints and without velocity measurements

Adaptive neural network control of robotic manipulators with input constraints and without velocity measurements

High-Precision Acquisition Method of Position Signal of Permanent Magnet Direct Drive Servo Motor at Low Speed

On the parametrization of algebraic differentiators in the light of ‘A survey on the discrete-time differentiators in closed-loop control systems: Experiments on an electro-pneumatic system’

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