The purpose of this paper is to reduce the strain and vibration during robotic machining.
Abstract
Purpose
The purpose of this paper is to reduce the strain and vibration during robotic machining.
Design/methodology/approach
An intelligent approach based on particle swarm optimization (PSO) and adaptive iteration algorithms is proposed to optimize the PD control parameters in accordance with robotic machining state.
Findings
The proposed intelligent approach can significantly reduce robotic machining strain and vibration.
Originality value
The relationship between robotic machining parameters is studied and the dynamics model of robotic machining is established. In view of the complexity of robotic machining process, the PSO and adaptive iteration algorithms are used to optimize the PD control parameters in accordance with robotic machining state. The PSO is used to optimize the PD control parameters during stable-machining state, and the adaptive iteration algorithm is used to optimize the PD control parameters during cut-into state.
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Keywords
Meng Xiao, Tie Zhang, Yanbiao Zou and Shouyan Chen
The purpose of this paper is to propose a robot constant grinding force control algorithm for the impact stage and processing stage of robotic grinding.
Abstract
Purpose
The purpose of this paper is to propose a robot constant grinding force control algorithm for the impact stage and processing stage of robotic grinding.
Design/methodology/approach
The robot constant grinding force control algorithm is based on a grinding model and iterative algorithm. During the impact stage, active disturbance rejection control is used to plan the robotic reference contact force, and the robot speed is adjusted according to the error between the robot’s real contact force and the robot’s reference contact force. In the processing stage, an RBF neural network is used to construct a model with the robot's position offset displacement and controlled output, and the increment of control parameters is estimated according to the RBF neural network model. The error of contact force and expected force converges gradually by iterating the control parameters online continuously.
Findings
The experimental results show that the normal force overshoot of the robot based on the grinding model and iterative algorithm is small, and the processing convergence speed is fast. The error between the normal force and the expected force is mostly within ±3 N. The normal force based on the force control algorithm is more stable than the normal force based on position control, and the surface roughness of the processed workpiece has also been improved, the Ra value compared with position control has been reduced by 24.2%.
Originality/value
As the proposed approach obtains a constant effect in the impact stage and processing stage of robot grinding and verified by the experiment, this approach can be used for robot grinding for improved machining accuracy.
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Michael Mace, Ravi Vaidyanathan, Shouyan Wang and Lalit Gupta
In this paper we describe a novel human machine interface system aimed primarily at those who have experienced loss of extremity motor function. The system enables the control of…
Abstract
In this paper we describe a novel human machine interface system aimed primarily at those who have experienced loss of extremity motor function. The system enables the control of a wide range of assistive technologies such as wheelchairs, prosthetics, computers and general electrical goods at the ‘flick of a tongue’. This system could benefit a huge sector of people including those who have suffered a spinal cord injury, stroke or quadriplegia.The technology focuses on a unique hands‐free interface whereby users can issue commands simply by performing subtle tongue movements; these tongue motions are continually monitored by a small microphone positioned comfortably within the ear canal. Due to the physiological connections between these regions and the distinctive nature of the signals, these commands can be detected and distinguished allowing a control signal to be issued.This inexpensive device offers significant advantages over existing technologies by providing unobtrusive, hygienic control through natural tongue motion. New software has been implemented, achieving over 97% correct classification across four different tongue movements for seven test subjects. Feasibility of the system as an interface for a variety of devices is demonstrated through simulation studies including controlling a prosthetic manipulator and power wheelchair.
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This study aims to propose a force control algorithm based on neural networks, which enables a robot to follow a changing reference force trajectory when in contact with human…
Abstract
Purpose
This study aims to propose a force control algorithm based on neural networks, which enables a robot to follow a changing reference force trajectory when in contact with human skin while maintaining a stable tracking force.
Design/methodology/approach
Aiming at the challenge of robots having difficulty tracking changing force trajectories in skin contact scenarios, a single neuron algorithm adaptive proportional – integral – derivative online compensation is used based on traditional impedance control. At the same time, to better adapt to changes in the skin contact environment, a gated recurrent unit (GRU) network is used to model and predict skin elasticity coefficients, thus adjusting to the uncertainty of skin environments.
Findings
In two robot–skin interaction experiments, compared with the traditional impedance control and robot force control algorithm based on the radial basis function model and iterative algorithm, the maximum absolute force error, the average absolute force error and the standard deviation of the force error are all decreased.
Research limitations/implications
As the training process of the GRU network is currently conducted offline, the focus in the subsequent phase is to refine the network to facilitate real-time computation of the algorithm.
Practical implications
This algorithm can be applied to robot massage, robot B-ultrasound and other robot-assisted treatment scenarios.
Originality/value
As the proposed approach obtains effective force tracking during robot–skin contact and is verified by the experiment, this approach can be used in robot–skin contact scenarios to enhance the accuracy of force application by a robot.