Constraint-Based and Sensorless Force Control With an Application to a Lightweight Dual-Arm Robot

Nicolis, Davide; Zanchettin, Andrea Maria; Rocco, Paolo

doi:10.1109/lra.2016.2517206

Cited by 20 publications

(10 citation statements)

References 24 publications

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“…where dual decision vector u ∈ R 2m and v ∈ R 4n are defined for equality constraint (38) and inequality constraint (39) respectively. In addition, the augmented matrix M and vector q are defined as…”

Section: Solutionmentioning

confidence: 99%

“…Solving the coordinated motion planning of dual manipulators is a difficult problem. The classical methods to solve coordinated operation motion of dual-robotic manipulators 36 are force control, 37,38 impedance method, 39 and intelligent control method. 40 In the field of the force control, Tsuji et al presented an algorithm for generating a variety of movements from planning trajectories.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tri-Criteria Optimization Motion Planning at Acceleration-Level of Dual Redundant Manipulators

Jia¹,

Chen²,

Zhang³

et al. 2019

Robotica

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SUMMARYIn order to solve joint-angle drift problem of dual redundant manipulators at acceleration-level, an acceleration-level tri-criteria optimization motion planning (ALTC-OMP) scheme is proposed, which combines the minimum acceleration norm, repetitive motion planning, and infinity-norm acceleration minimization solutions via weighting factor. This scheme can resolve the joint-angle drift problem of dual redundant manipulators which will arise in single criteria or bi-criteria scheme. In addition, the proposed scheme considers joint-velocity joint-acceleration physical limits. The proposed scheme can not only guarantee joint-velocity and joint-acceleration within their physical limits, but also ensure that final joint-velocity and joint-acceleration are near to zero. This scheme is realized by dual redundant manipulators which consist of left and right manipulators. In order to ensure the coordinated operation of manipulators, two motion planning problems are reformulated as two general quadratic program (QP) problems and further unified into one standard QP problem, which is solved by a simplified linear-variational-inequalities-based primal-dual neural network at the acceleration-level. Computer-simulation results based on dual PUMA560 redundant manipulators further demonstrate the effectiveness and feasibility of the proposed ALTC-OMP scheme to resolve joint-angle drift problem arising in the dual redundant manipulators.

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Section: Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tri-Criteria Optimization Motion Planning at Acceleration-Level of Dual Redundant Manipulators

Jia¹,

Chen²,

Zhang³

et al. 2019

Robotica

View full text Add to dashboard Cite

show abstract

“…These tasks need the robot controllers to maintain satisfactory dynamic behavior in spite of possible external perturbations, unknown dynamic parameters, and sensor information loss (Armendariz et al, 2014 ). Several controllers that are often implemented to manage these features are also mentioned (Luo and Kuo, 2016 ; Makarov et al, 2016 ; Nicolis et al, 2016 ; Pan et al, 2018 ; Hwang and Yu, 2020 ). Over time, the Proportional Integral Derivative (PID) control has been used to design industrial robots due to their simple structure and simple hardware implementation.…”

Section: Introductionmentioning

confidence: 99%

PD Control Compensation Based on a Cascade Neural Network Applied to a Robot Manipulator

et al. 2020

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A Proportional Integral Derivative (PID) controller is commonly used to carry out tasks like position tracking in the industrial robot manipulator controller; however, over time, the PID integral gain generates degradation within the controller, which then produces reduced stability and bandwidth. A proportional derivative (PD) controller has been proposed to deal with the increase in integral gain but is limited if gravity is not compensated for. In practice, the dynamic system non-linearities frequently are unknown or hard to obtain. Adaptive controllers are online schemes that are used to deal with systems that present non-linear and uncertainties dynamics. Adaptive controller use measured data of system trajectory in order to learn and compensate the uncertainties and external disturbances. However, these techniques can adopt more efficient learning methods in order to improve their performance. In this work, a nominal control law is used to achieve a sub-optimal performance, and a scheme based on a cascade neural network is implemented to act as a non-linear compensation whose task is to improve upon the performance of the nominal controller. The main contributions of this work are neural compensation based on a cascade neural networks and the function to update the weights of neural network used. The algorithm is implemented using radial basis function neural networks and a recompense function that leads longer traces for an identification problem. A two-degree-of-freedom robot manipulator is proposed to validate the proposed scheme and compare it with conventional PD control compensation.

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“…3 Nicolis et al employed a control framework for the sensorless execution of a force control task with an application for a lightweight dual-arm robot. 5 Ragaglia et al introduced a novel approach based on an observer with a joint position, joint velocity, and electric current measure that did not rely on the dedicated hardware torque. This was due to the operator power and its ability to conduct arm movement experiments shown in an ABB's robotic dual-arm concept FRIDA.…”

Section: Introductionmentioning

confidence: 99%

Sliding mode nonlinear disturbance observer-based adaptive back-stepping control of a humanoid robotic dual manipulator

Bai

Gong

Chen³

et al. 2018

Robotica

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SUMMARYAn adaptive back-stepping sliding mode controller (ABSMC) algorithm was developed for nonlinear uncertain systems based on a nonlinear disturbance observer (NDO). The developed ABSMC was applied to attitude control for the dual arm of a humanoid robot. Considering the system uncertainty and the unknown external disturbances, the ABSMC scheme was designed to eliminate the chattering phenomenon in the traditional sliding mode control and to reduce the tracking error closer to zero. The ABSMC algorithm solved problems related to the chattering of the system for both uncertainties and disturbances in the humanoid robotic system with an NDO in a two-dimensional environment. The algorithm was designed to work equally well with agents, with higher degrees of freedom in different applications. The method was appropriate for improving tracking performance. The ABSMC algorithm guaranteed global stability and improved the dynamic performance of the system. The algorithm inherited a low computational cost, probabilistic completeness, and asymptotic optimality from the fuzzy sliding mode control. This algorithm has a practical application in the dual arm of a humanoid robot with a circular trajectory. This paper showed the effectiveness and applicability of the proposed methods, which reduced the output of the controller and improved the control performance of the humanoid robotic system. The new combined control algorithm, ABSMC, was able to feasibly and efficiently weaken the chattering on the robot's closed-loop paths, starting and finishing at the same configuration.

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Constraint-Based and Sensorless Force Control With an Application to a Lightweight Dual-Arm Robot

Cited by 20 publications

References 24 publications

Tri-Criteria Optimization Motion Planning at Acceleration-Level of Dual Redundant Manipulators

Tri-Criteria Optimization Motion Planning at Acceleration-Level of Dual Redundant Manipulators

PD Control Compensation Based on a Cascade Neural Network Applied to a Robot Manipulator

Sliding mode nonlinear disturbance observer-based adaptive back-stepping control of a humanoid robotic dual manipulator

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