Abstract
The low stiffness has limited the applications of robot to machining process. In this paper, a fuzzy-sliding mode control scheme is proposed to manage the oscillation and chatter appearing in machining operation by adjusting the feed rate. The robotic machining dynamics is first analyzed to identify the parameters with focus on the system stiffness and the behavior during machining process. A controller consisting of a fuzzy estimation enginery which can determine the control gain coefficients according to system status and a sliding mode controller which is used to guarantee convergence and global stability of the system is then proposed. Simulations and experiments results show that, in comparison with open loop and fuzzy-PID control scheme, the fuzzy-sliding mode control scheme can reduce the amplitude and period of oscillation.
Highlights
Industrial robot is worldwide applied in many fields such as material transfer, machining, and assembling [1,2,3]
As for machining, many studies have been reported and the results indicated that some critical issues, including trajectory error, material removal rate, and contacting force, are needed to be addressed
To identify the relationship between removal rate and contacting force, Domroes and Krewet [4] compared the performance of force dependent feed rate control and orthogonal force control and reached a conclusion that the removal rate at constant force varies around a mean value
Summary
Industrial robot is worldwide applied in many fields such as material transfer, machining, and assembling [1,2,3]. To identify the relationship between removal rate and contacting force, Domroes and Krewet [4] compared the performance of force dependent feed rate control and orthogonal force control and reached a conclusion that the removal rate at constant force varies around a mean value. This statement is agreed upon by Karayiannidis and Doulgeri [5], who proposed an adaptive leaning controller to identify the surface condition with the use of force and joint position/velocity measurements. For unknown object and environment, Kiguchi and Fukuda [6] proposed an intelligent controller which was constructed on the basis of adaptive fuzzy neural position/force strategy, while Zarko and Vlastimir [7] proposed adaptive neurofuzzy-genetic control schemes for explicit force robot control
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