MPC‐based robust contouring control for a robotic machining system

Kornmaneesang, Woraphrut; Chen, Shyh‐Leh

doi:10.1002/asjc.2429

Cited by 12 publications

(5 citation statements)

References 35 publications

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“…9 plots the tangential motion profiles, including velocity, acceleration, and jerk, with respect to time. Through kinematics chain of the machining system found in [23], the joint motion profiles are obtained and presented in Fig. 10.…”

Section: Simulation and Experimental Resultsmentioning

confidence: 99%

Time-optimal feedrate scheduling with actuator constraints for 5-axis machining

Kornmaneesang

Chen

2022

Int J Adv Manuf Technol

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Cycle time minimization is one of the major goals that many manufacturers are eager to achieve. Maximizing feedrate is the direct solution, however, physical motions need to be under the specified motion limits to avoid high-frequency vibration, causing machining error. In this paper, a time-optimal feedrate scheduling approach for 5-axis G1 toolpath is presented for 5-axis machining. A quintic B-spline corner smoothing method is utilized to smoothen sharp corners in the toolpath. Then, the S-shape feedrate profile of each block is optimized under the actuator motion constraints, with the objective of minimizing the cycle time. Particle swarm optimization (PSO) is used to provide the optimized solution. Experiments are conducted to validate the proposed approach and the results are compared with two other existing approaches. It is found that the proposed method can achieve shorter cycle time and less contour errors, showing the effectiveness of the proposed approach.

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Section: Simulation and Experimental Resultsmentioning

confidence: 99%

Time-optimal feedrate scheduling with actuator constraints for 5-axis machining

Kornmaneesang

Chen

2022

Int J Adv Manuf Technol

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“…An integrated control strategy of cross-coupling contour error compensation based on chord error constraint, which consists of a cross-coupling controller and an improved position error compensator, was proposed by Zhang et al 8 . Kommaneesang et al 9 pointed that only a few researchers concentrate on solving the contouring problem in the robotic machining system, and the contouring control problem was transformed into the regulation problem by using the method of equivalent errors. Moreover, contour accuracy control was studied in many literatures 10 – 12 .…”

Section: Introductionmentioning

confidence: 99%

“…Compared with the method of equivalent errors in Ref. 9 , the accuracy of joint error and contour error is guaranteed simultaneously. Furthermore, a unified framework of cross-coupling contour compensation and reference position precompensation is built.…”

Section: Introductionmentioning

confidence: 99%

Adaptive nonsingular terminal sliding mode control of robot manipulator based on contour error compensation

Zhu

Huang

et al. 2023

Sci Rep

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To achieve accurate contour tracking of robotic manipulators with system uncertainties, external disturbance and actuator faults, a cross-coupling contour adaptive nonsingular terminal sliding mode control (CCCANTSMC) is proposed. A nonsingular terminal sliding mode manifold is developed which eliminates the singularity completely. In order to avoid the demand of the prior knowledge of system uncertainties, external disturbance and actuator faults in practical applications, an adaptive tuning approach is proposed. The stability of the proposed control strategy is demonstrated by the finite-time stability theory. Then, the developed controller combines adaptive nonlinear terminal sliding mode control (ANTSMC) of joint trajectory tracking and proportion–differentiation control of end-effector contour tracking by introducing the coupling factor between multiple axes based on Jacobian. Moreover, a unified framework of cross-coupling contour compensation and reference position pre-compensation is built. Finally, numerical simulation and experimental results validate the effectiveness of the proposed control strategy.

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“…Industrial robot plays an important role for intelligent automation. It has been widely used in industries, for example, composite manufacturing [1], painting [2], automotive assembly lines [3], machining [4], etc. Recently, industrial robot has been integrated into manufacturing cell, the robot will automatically load the workpiece to the machine tool, and the machine tool will perform the machining task.…”

Section: Introductionmentioning

confidence: 99%

Iterative learning control for integrated system of robot and machine tool

Chen

2022

Asian Journal of Control

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This study is concerned with the integrated system of a robot and a machine tool. The major task of robot is loading the workpiece to the machine tool for contour cutting. An iterative learning control (ILC) algorithm is proposed to improve the accuracy of the finished product. The proposed ILC is to modify the input command of the next machining cycle for both robot and machine tool to iteratively enhance the output accuracy of the robot and machine tool. The modified command is computed based on the current tracking/contour error. For the ILC of the robot, tracking error is considered as the control objective to reduce the tracking error of motion path, in particular, the error at the endpoint. Meanwhile, for the ILC of the machine tool, contour error is considered as the control objective to improve the contouring accuracy, which determines the quality of machining. In view of the complicated contour error model, the equivalent contour error instead of the actual contour error is taken as the control objective in this study. One challenge for the integrated system is that there exists an initial state error for the machine tool dynamics, violating the basic assumption of ILC. It will be shown in this study that the effects of initial state error can be significantly reduced by the ILC of the robot. The proposed ILC algorithm is verified experimentally on an integrated system of commercial robot and machine tool. The experimental results show that the proposed ILC can achieve more than 90% of reduction on both the RMS tracking error of the robot and the RMS contour error of the machine tool within six learning iterations. The results clearly validate the effectiveness of the proposed ILC for the integrated system.

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MPC‐based robust contouring control for a robotic machining system

Cited by 12 publications

References 35 publications

Time-optimal feedrate scheduling with actuator constraints for 5-axis machining

Time-optimal feedrate scheduling with actuator constraints for 5-axis machining

Adaptive nonsingular terminal sliding mode control of robot manipulator based on contour error compensation

Iterative learning control for integrated system of robot and machine tool

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