Contact force and torque sensing for serial manipulator based on an adaptive Kalman filter with variable time period

Cao, Feng; Docherty, Paul; Ni, Siliang; Chen, Xiaoqi

doi:10.1016/j.rcim.2021.102210

Cited by 22 publications

(8 citation statements)

References 58 publications

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“…The adaptive Kalman filter (AKF) is regarded as an effective method to decrease the estimation noise. Cao et al [30] combined a modeswitching moving average based on a variable period with a classical Kalman filter to handle the measurement noise. Furthermore, Zhu et al [31] proposed a variational Bayesian AKF to address the issue of state estimation with an inaccurate nominal process and measurement noise covariance.…”

Section: Introductionmentioning

confidence: 99%

Active Compliance Control of a Position-Controlled Industrial Robot for Simulating Space Operations

Shen

Gao

et al. 2022

Chin. J. Mech. Eng.

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An industrial robot with a six-axis force/torque sensor is usually used to produce a zero-gravity environment for testing space robotic operations. However, using traditional force control methods, such as admittance control, causes position-controlled industrial robots to undergo from force divergence owing to intrinsic time delay. In this paper, a new force control method is proposed to eliminate the force divergence. A hardware-in-the-loop (HIL) simulator with an industrial robot is first presented. The free-floating satellite dynamics and the motion mapping from the satellites to simulator are both established. Thus, the effects of measurement delay and dynamic response delay on contact velocity and force are investigated. After that, a real-time estimation method for contact stiffness and damping is proposed based on the adaptive Kalman filter. The measurement delay is compensated by a phase lead model. Moreover, the identified contact parameters are adopted to modify contact forces, and thus the dynamics response delay can be compensated for. Finally, a co-simulation and experiments were conducted to verify the force control method. The results show that contact stiffness and damping could be identified exactly and that the simulation divergence could be prevented. This paper proposes an active compliance control method that can deal with force constrained tasks of a position-controlled robot in unknown environments.

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Section: Introductionmentioning

confidence: 99%

Active Compliance Control of a Position-Controlled Industrial Robot for Simulating Space Operations

Shen

Gao

et al. 2022

Chin. J. Mech. Eng.

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“…In recent years, many methods to identify load torque have been mentioned in relevant literature. Common load torque identification methods include Kalman filter [ 15 , 16 ], reduced-order observer [ 17 , 18 ], and adaptive control strategy based on disturbance observer [ 19 , 20 ]. In [ 21 ], after considering the extended Kalman filter and the unscented Kalman filter, an extended nonlinear Kalman filter is proposed for online coupling and estimation of the load.…”

Section: Introductionmentioning

confidence: 99%

RBFNN-Enabled Adaptive Parameters Identification for Robot Servo System Based on Improved Sliding Mode Observer

Wang

et al. 2022

Computational Intelligence and Neuroscience

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Effective and accurate parameter identification, especially the identification of load torque, is one of the key factors to improve the control performance of the robot servo system. Sliding mode observer (SMO) has always been a common method for identifying load torque due to its advantages of simple implementation, strong robustness, and fast response. However, due to the discontinuity of the SMO switching function, the system will generate high-frequency chattering, which will reduce the accuracy of load torque identification and affect system performance. In this paper, an adaptive parameter identification method based on an improved sliding mode observer is proposed. A continuous deformation mode of saturation function based on boundary variation is proposed as the switching function to alleviate the chattering phenomenon. Meanwhile, the relationship between the sliding mode gain and the feedback gain of proposed SMO is defined so that it can be selected properly to improve the accuracy of identification, and the radial basis function neural network (RBFNN) is used to adaptively tune the boundary layer gain according to the speed change. Moreover, considering that the identification result of the load torque is related to the moment of inertia and the mismatch of the inertia will cause identification errors, the variable period integration method is proposed to identify the inertia and redefine the calculation period of the load torque and inertia. The effectiveness and superiority of the proposed method are verified by simulation experiments. Experimental results demonstrate that the improved SMO combines observer gain coefficient tuning and inertia matching can smoothly and accurately estimate the value of load torque, which is an adaptive identification method worthy of reference for robot servo system.

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“…The impedance parameter adaptations are mainly adjusted according to real‐time changes such as contact force, motion, 14 virtual fixture (VF), 15 etc. Generally, the ways to improve the accuracy of the control system mainly include establishing the system nonlinear friction model, 16,17 establishing the dynamic model, 15,18,19 and reducing the signal noise, 20 etc. Dutta et al.…”

Section: Introductionmentioning

confidence: 99%

“…Cao et al. proposed an external force estimation based on momentum observer and adopted the adaptive Kalman filter to reduce current measurement noise and improve the external force estimation accuracy 20 . Magrini et al.…”

Section: Introductionmentioning

confidence: 99%

“…The impedance parameter adaptations are mainly adjusted according to real-time changes such as contact force, motion, 14 virtual fixture (VF), 15 etc. Generally, the ways to improve the accuracy of the control system mainly include establishing the system nonlinear friction model, 16,17 establishing the dynamic model, 15,18,19 and reducing the signal noise, 20 etc. Dutta et al proposed a sensorless full-body active compliant 6-RSS parallel manipulator, which realizes the contact force estimation through the joint torque, and designs a three-layer impedance controller.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive variable impedance position/force tracking control of fracture reduction robot

Zheng

Lei

et al. 2022

Robotics Computer Surgery

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Background The operation object of robot‐assisted fracture reduction surgery is the musculoskeletal tissue with rigid‐compliance coupling characteristics. It is necessary to improve the interactive compliance and safety between the reduction robot and the musculoskeletal tissue. Method An adaptive variable impedance position/force tracking control strategy based on friction compensation is proposed. The stiffness of the reduction robot can be adaptively adjusted according to the contact force between the end‐effector and the environment. The Stribeck friction force model of the branch chain electric cylinder is derived to improve the motion control performance. Results The fracture reduction experiment is completed. The experimental results show that the adaptive variable impedance position/force control strategy can realize position and force tracking in fracture reduction. Conclusion A safety control strategy is proposed and applied to robot‐assisted fracture reduction surgery, which improves the coordination and compliance of the human‐robot interaction between the reduction robot and the patient.

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Contact force and torque sensing for serial manipulator based on an adaptive Kalman filter with variable time period

Cited by 22 publications

References 58 publications

Active Compliance Control of a Position-Controlled Industrial Robot for Simulating Space Operations

Active Compliance Control of a Position-Controlled Industrial Robot for Simulating Space Operations

RBFNN-Enabled Adaptive Parameters Identification for Robot Servo System Based on Improved Sliding Mode Observer

Adaptive variable impedance position/force tracking control of fracture reduction robot

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