Stability analysis of robust adaptive hybrid position/force controller for robot manipulators using neural network with uncertainties

Singh, Harendra; Sukavanam, N.

doi:10.1007/s00521-012-0966-6

Cited by 44 publications

(23 citation statements)

References 14 publications

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“…Progressively this theme has been growing; for instance, [35] describes the design and implementation of a hybrid force/motion scheme to control the interaction force of a robot manipulator, using a PD controller with gain-scheduled linear parameter-varying controller; however, although the reference for this control scheme does not include a stability proof, it presents experimental results on a six-degree-of-freedom manipulator. The paper [36] proposes the design of a robust adaptive neural networkbased hybrid position/force control with PD-type structure for manipulators. It includes a stability analysis in the sense of Lyapunov, but only simulation results with a model of a two-link robot manipulator are presented.…”

Section: Journal Of Roboticsmentioning

confidence: 99%

A Family of Hyperbolic-Type Explicit Force Regulators with Active Velocity Damping for Robot Manipulators

Reyes-Cortés

Chávez-Olivares

González-Galván

2018

Journal of Robotics

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This paper addresses the explicit force regulation problem for robot manipulators in interaction tasks. A new family of explicit force-control schemes is presented, which includes a term driven by a large class of saturated-type hyperbolic functions to handle the force error. Also, an active velocity damping term with the purpose of obtaining energy dissipation on the contact surface is incorporated plus compensation for gravity. In order to ensure asymptotic stability of the closed-loop system equilibrium point in Cartesian space, we propose a strict Lyapunov function. A force sensor placed at the end-effector of the robot manipulator is used in order to feed back the measure of the force error in the closed-loop, and an experimental comparison of the performanceL2-norm between 5 explicit force control schemes, which are the classical proportional-derivative (PD), arctangent, and square-root controls and two members of the proposed control family, on a two-degree-of-freedom, direct-drive robot manipulator, is presented.

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Section: Journal Of Roboticsmentioning

confidence: 99%

A Family of Hyperbolic-Type Explicit Force Regulators with Active Velocity Damping for Robot Manipulators

Reyes-Cortés

Chávez-Olivares

González-Galván

2018

Journal of Robotics

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“…Recently, many researchers tried to introduce the intelligent control methods into the force control to improve the tracking performance and robustness of the robotic system [15][16][17][18][19][20]. In [21], the neural network control technique was applied in impedance controller to compensate the uncertainties in an online manner.…”

Section: Introductionmentioning

confidence: 99%

Position/Force Tracking Impedance Control for Robotic Systems with Uncertainties Based on Adaptive Jacobian and Neural Network

Peng

Yang

2019

Complexity

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In this paper, an adaptive Jacobian and neural network based position/force tracking impedance control scheme is proposed for controlling robotic systems with uncertainties and external disturbances. To achieve precise force control performance indirectly by using the position tracking, the control scheme is divided into two parts: the outer-loop force impedance control and the inner-loop position tracking control. In the outer-loop, an improved impedance controller, which combines the traditional impedance relationship with the PID-like scheme, is designed to eliminate the force tracking error quickly and to reduce the force overshoot effectively. In this way, the satisfied force tracking performance can be achieved when the manipulator contacts with environment. In the inner-loop, an adaptive Jacobian method is proposed to estimate the velocities and interaction torques of the end-effector due to the system kinematical uncertainties, and the system dynamical uncertainties and the uncertain term of adaptive Jacobian are compensated by an adaptive radial basis function neural network (RBFNN). Then, a robust term is designed to compensate the external disturbances and the approximation errors of RBFNN. In this way, the command position trajectories generated from the outer-loop force impedance controller can be then tracked so that the contact force tracking performance can be achieved indirectly in the forced direction. Based on the Lyapunov stability theorem, it is proved that all the signals in closed-loop system are bounded and the position and velocity errors are asymptotic convergence to zero. Finally, the validity of the control scheme is shown by computer simulation on a two-link robotic manipulator.

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“…Due to these uncertainties system error becomes large, when a robot manipulator operates at high speed. It is a challenging problem in control field to find an effective control scheme to achieve accurate tracking of the desired motion (Khoygani et al, 2015;Singh et al, 2013;Han et al, 2015;Cuong et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Neural Network Disturbance Observer-Based Adaptive Tracking Control for Robot Manipulator

Huang¹,

Guo²,

Qu³

2017

IJCA

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Stability analysis of robust adaptive hybrid position/force controller for robot manipulators using neural network with uncertainties

Cited by 44 publications

References 14 publications

A Family of Hyperbolic-Type Explicit Force Regulators with Active Velocity Damping for Robot Manipulators

A Family of Hyperbolic-Type Explicit Force Regulators with Active Velocity Damping for Robot Manipulators

Position/Force Tracking Impedance Control for Robotic Systems with Uncertainties Based on Adaptive Jacobian and Neural Network

Neural Network Disturbance Observer-Based Adaptive Tracking Control for Robot Manipulator

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