Abstract
The actuator dead zone of free-form surface grinding robots (FFSGRs) is very common in the grinding process and has a great impact on the grinding quality of a workpiece. In this paper, an improved trajectory tracking algorithm for an FFSGR with an asymmetric actuator dead zone was proposed with consideration of friction forces, model uncertainties, and external disturbances. The presented control algorithm was based on the machine learning and sliding mode control (SMC) methods. The control compensator used neural networks to estimate the actuator’s dead zone and eliminate its effects. The robust SMC compensator acted as an auxiliary controller to guarantee the system’s stability and robustness under circumstances with model uncertainties, approximation errors, and friction forces. The stability of the closed-loop system and the asymptotic convergence of tracking errors were evaluated using Lyapunov theory. The simulation results showed that the dead zone’s non-linearity can be estimated correctly, and satisfactory trajectory tracking performance can be obtained in this way, since the influences of the actuator’s dead zone were eliminated. The convergence time of the system was reduced from 1.1 to 0.8 s, and the maximum steady-state error was reduced from 0.06 to 0.015 rad. In the grinding experiment, the joint steady-state error decreased by 21%, which proves the feasibility and effectiveness of the proposed control method.
Highlights
In many manufacturing fields, the processing of free-form surfaces is a challenging problem [1](e.g., in aircraft engines, marine propellers, and sanitary equipment)
Based on the existing literature, this paper further studied the trajectory tracking control problem of free-form surface grinding robots (FFSGRs) with actuator dead zone non-linearity
An adaptive robust sliding mode control algorithm was proposed for the considered system, and radial basis function neural network (RBFNN)-based machine learning technology was introduced for the estimation and mitigation of an actuator’s dead zone
Summary
Lin Jia 1,2 , Yaonan Wang 1,2 , Changfan Zhang 3, *, Kaihui Zhao 3 , Li Liu 1,2 and. National Engineering Laboratory for Robot Visual Perception and Control, Hunan University, Changsha 410082, China. Faculty of Electrical Engineering Technology, Hanoi University of Industry, Hanoi 100000, Vietnam. Featured Application: The grinding robot and its control system described in this paper can be used for automatic grinding of free-form surfaces, such as engine blades, aircraft wings, propeller blades, fan blades, high-speed rail bogies, automotive engine cylinders, etc
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