GMS friction compensation in robot manipulator

Grami, Said; Gharbia, Yousef

doi:10.1109/iecon.2013.6699700

Cited by 9 publications

(6 citation statements)

References 13 publications

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“…Many models have been proposed, for example, the Coulomb and viscous friction model, 5 the LuGre friction model, 24 the Stribeck friction model, 25 the cubic polynomial model, and the generalized maxwell slip (GMS) model. 26,27 Torque can be identified by obtaining data from a joint torque sensor or calculated using a dynamic model. Here, however, we present a novel method of identification which uses an external torque observer that does not change a robot’s physical structure or import any differential noise.…”

Section: Identifying Friction In the Jointmentioning

confidence: 99%

Collision detection algorithm for collaborative robots considering joint friction

Xiao

Zhang

Hong

et al. 2018

International Journal of Advanced Robotic Systems

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This article proposes a collision detection algorithm without external sensors that can detect potential collisions in manrobot interaction. The algorithm is based on a modified first-order momentum deviation observer that also takes friction into account. The collision detection algorithm uses joint angles, angular velocities, and torques during the detection process, without any need to consider angular acceleration. The algorithm also uses an accurate friction model that is based on a Stribeck model with second-order Fourier series compensation. The friction model is applied in advance so that compensation can be made in real time during collision detection. Identification data are filtered through a first-order low-pass filter to reduce high-frequency noise. In order to verify the algorithm, a simulation and experiment were carried out using a collaborative robot experimental platform. The results confirmed that collisions can be detected by setting appropriate threshold values. Different possible responses can be implemented according to different response strategies, with the ultimate arbiter being that collision forces are kept strictly within ordinary human tolerances. This makes sure that safety can be preserved in man-robot interaction processes.

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Section: Identifying Friction In the Jointmentioning

confidence: 99%

Collision detection algorithm for collaborative robots considering joint friction

Xiao

Zhang

Hong

et al. 2018

International Journal of Advanced Robotic Systems

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show abstract

“…In these methodologies, the friction model parameters are tuned online to obtain a satisfactory compensation action. [29][30][31][32][33] However, friction modeling is a challenge since the friction behavior is highly nonlinear. Therefore, the tendency is to develop more detailed friction models to evoke the online friction compensation procedure.…”

Section: Related Workmentioning

confidence: 99%

Joint friction estimation for walking bipeds

Hashlamon¹,

Erbatur²

2014

Robotica

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SUMMARYThis paper proposed a new approach for the joint friction estimation of non-slipping walking biped robots. The proposed approach is based on the combination of a measurement-based strategy and a model-based method. The former is used to estimate the joint friction online when the foot is in contact with the ground, while the latter adopts a friction model to represent the joint friction when the leg is swinging. The measurement-based strategy utilizes the measured ground reaction forces (GRF) and the readings of an inertial measurement unit (IMU) located at the robot body. Based on these measurements, the joint angular accelerations and the body attitude and velocity are estimated. The aforementioned measurements and estimates are used in a reduced dynamical model of the biped. However, when the leg is swinging, this strategy is inapplicable. Therefore, a friction model is adopted. Its parameters are identified adaptively using the estimated online friction whenever the foot is in contact. The estimated joint friction is used in the feedback torque control signal. The proposed approach is validated using the full-dynamics of 12-DOF biped model. By using this approach, the robot center of mass (CoM) position error is reduced by 10% which demonstrates the effectiveness of this approach.

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“…The generalized Maxwell-slip (GMS) friction compensation in a two-DoF robotic manipulator utilized an online least-squares estimator to estimate the friction force in each joint [29]. A proportional derivative (PD) controller was illustrated in [30] with friction compensation. The adaptive sliding control (A-SC) algorithm with friction compensation for robotic manipulator established on fuzzy random vector function is described in [31].…”

Section: Introductionmentioning

confidence: 99%

Adaptive FIT-SMC Approach for an Anthropomorphic Manipulator With Robust Exact Differentiator and Neural Network-Based Friction Compensation

et al. 2022

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In robotic manipulators, feedback control of nonlinear systems with fast finite-time convergence is desirable. However, because of the parametric and model uncertainties, the robust control and tuning of the robotic manipulators pose many challenges related to the trajectory tracking of the robotic system. This research proposes a state-of-the-art control algorithm, which is the combination of fast integral terminal sliding mode control (FIT-SMC), robust exact differentiator (RED) observer, and feedforward neural network (FFNN) based estimator. Firstly, the dynamic model of the robotic manipulator is established for the n degrees of freedom (DoFs) system by taking into account the dynamic LuGre friction model. Then, a FIT-SMC with friction compensation-based nonlinear control has been proposed for the robotic manipulator. In addition, a RED observer is developed to get the estimates of robotic manipulator joints' velocities. Since the dynamic friction state of the LuGre friction model is unmeasurable, FFNN is established for training and estimating the friction torque. The Lyapunov method is presented to demonstrate the finite-time sliding mode enforcement and state convergence for a robotic manipulator. The proposed control approach has been simulated in the MATLAB/Simulink environment and compared with the system with no observer to characterize the control performance. Simulation results obtained with the proposed control strategy affirm its effectiveness for a multi-DoF robotic system with model-based friction compensation having an overshoot and a settling time less than 1.5% and 0.2950 seconds, respectively, for all the joints of the robotic manipulator.INDEX TERMS robotic manipulator, robust exact differentiator, feedforward neural network, fast integral sliding mode control, LuGre friction model, autonomous articulated robotic educational platform.

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GMS friction compensation in robot manipulator

Cited by 9 publications

References 13 publications

Collision detection algorithm for collaborative robots considering joint friction

Collision detection algorithm for collaborative robots considering joint friction

Joint friction estimation for walking bipeds

Adaptive FIT-SMC Approach for an Anthropomorphic Manipulator With Robust Exact Differentiator and Neural Network-Based Friction Compensation

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