High-performance parallel hexapod-robotic light abrasive grinding using real-time tool deflection compensation and constant resultant force control

Latifinavid, Masoud; Donder, Abdulhamit; Konukseven, Erhan İlhan

doi:10.1007/s00170-018-1838-8

Cited by 20 publications

(10 citation statements)

References 35 publications

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“…For instance, Masoud et al proposed a resultant force control strategy for light abrasive surface grinding. The performance of resultant force control strategy with real-time tool deflection compensation algorithm is validated by the grinding experimental studies [13]. Wang et al designed a vibration suppression method for large thin-walled shell grinding based on force control algorithm.…”

Section: Introductionmentioning

confidence: 99%

Design of a spatial constant-force end-effector for polishing/deburring operations

Ding

Zhao

2021

Int J Adv Manuf Technol

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Controlling the contact force on workpieces is a challenging task for industrial deburring operations. To solve this issue, a novel constant force mechanism (CFM) based on the combination of positive and negative stiffness mechanism is proposed by using folding beam and bi-stable beam mechanisms. Without using any additional sensors and control algorithms, the proposed CFM can produce a travel range in constant force manner. In this paper, the design concepts, analytical model, finite element analysis (FEA) simulation and experimental studies are presented and discussed. Firstly, a novel spatial CFM is proposed and the pseudo rigid body (PRB) method is used to establish the mathematical model of the whole mechanism. Then, the FEA simulation is performed to validate the correctness of theoretical analysis. In addition, to eliminate the force variation, particle swarm optimization (PSO) method is utilized to find optimal architectural parameters solutions of the CFM. Finally, the experimental tests are performed to verify the performance of the designed CFM. The configuration design and parameter optimization proposed in this paper can be further applied to the design of other types of CFM mechanisms for polishing operations as well.

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Section: Introductionmentioning

confidence: 99%

Design of a spatial constant-force end-effector for polishing/deburring operations

Ding

Zhao

2021

Int J Adv Manuf Technol

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“…In order to solve the influence of various uncertain factors in manipulator system, a variety of observers were proposed in [20] to deal with uncertain kinematics and estimate unknown torque, respectively. References [21,22] have proposed a real-time online error compensation method for the manipulator, which avoids frequent calibration of sensors in the actual industrial application scene and reduces the complexity of the control system. By using the filter to generate uniform B-spline trajectory, the tracking error at the desired path point can be eliminated, and the trajectory error at the end of the manipulator can be compensated without modifying the core content of the controller [23].…”

Section: Introductionmentioning

confidence: 99%

Repetitive Control Scheme of Robotic Manipulators Based on Improved B‐Spline Function

Wang

et al. 2021

Complexity

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In this paper, a repetitive control scheme of a 2-DOF robotic manipulator based on the improved cubic B-spline curve is proposed. Firstly, a repetitive controller for robotic manipulator is designed, which is composed of an iterative controller and disturbance observer. Then, an improved B-spline optimization scheme is introduced to divide the task of the robotic manipulator into three intervals. A correction function is added to each interval of cubic spline interpolation. Finally, a variety of cases are designed and simulated by MATLAB. The experimental results show that, compared with the conventional B-spline, the improved B-spline has better performance in tracking accuracy and smoothness of motion trajectory. By changing the mechanism of the manipulator, the cases of different weights and lengths are designed. The experimental results in these cases show that the proposed scheme can be applied to most of the 2-DOF robotic manipulator control systems.

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“…In some cases, the dead zone and saturation problem can be addressed by nonlinear or adaptive control laws. For example, [19] shows an approach using fuzzy control with Gaussian membership functions, which is in practice similar to the RFWR method, and [20] describes a gain-scheduling adaptive approach to deal with internal system bounds. Using RL to find a control law for a nonlinear system also has the advantage that it can often deal with such disturbances on its own through the optimization process; for example, only a limited range of the actor outputs may be limited, which is the approach utilized in this paper.…”

Section: Introductionmentioning

confidence: 99%

Control of Magnetic Manipulator Using Reinforcement Learning Based on Incrementally Adapted Local Linear Models

et al. 2021

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Reinforcement learning (RL) agents can learn to control a nonlinear system without using a model of the system. However, having a model brings benefits, mainly in terms of a reduced number of unsuccessful trials before achieving acceptable control performance. Several modelling approaches have been used in the RL domain, such as neural networks, local linear regression, or Gaussian processes. In this article, we focus on techniques that have not been used much so far: symbolic regression (SR), based on genetic programming and local modelling. Using measured data, symbolic regression yields a nonlinear, continuous-time analytic model. We benchmark two state-of-the-art methods, SNGP (single-node genetic programming) and MGGP (multigene genetic programming), against a standard incremental local regression method called RFWR (receptive field weighted regression). We have introduced modifications to the RFWR algorithm to better suit the low-dimensional continuous-time systems we are mostly dealing with. The benchmark is a nonlinear, dynamic magnetic manipulation system. The results show that using the RL framework and a suitable approximation method, it is possible to design a stable controller of such a complex system without the necessity of any haphazard learning. While all of the approximation methods were successful, MGGP achieved the best results at the cost of higher computational complexity. Index Terms–AI-based methods, local linear regression, nonlinear systems, magnetic manipulation, model learning for control, optimal control, reinforcement learning, symbolic regression.

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High-performance parallel hexapod-robotic light abrasive grinding using real-time tool deflection compensation and constant resultant force control

Cited by 20 publications

References 35 publications

Design of a spatial constant-force end-effector for polishing/deburring operations

Design of a spatial constant-force end-effector for polishing/deburring operations

Repetitive Control Scheme of Robotic Manipulators Based on Improved B‐Spline Function

Control of Magnetic Manipulator Using Reinforcement Learning Based on Incrementally Adapted Local Linear Models

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