Adaptive SMC for trajectory tracking in freeform grinding
This work is a first step to the automation of freeform surface grinding. A control strategy for a robotic manipulator following a path which includes switching between different surfaces, constant depths of cut and system-inherent as well as environmental uncertainties is presented. The sliding mode control scheme with adaptive parameter update law is verified through simulation.
Adaptive trajectory tracking neural network control with robust compensator for robot manipulators
This paper presents an adaptive trajectory tracking neural network control using radial basis function (RBF) for an n-link robot manipulator with robust compensator to achieve the high-precision position tracking. One of the difficulties in designing a suitable control scheme which can achieve accurate trajectory tracking and good control performance is to guarantee the stability and robustness of control system, due to friction forces, external disturbances error, and parameter variations. To deal with this problem, the RBF network is investigated to the joint position control of an n-link robot manipulator. The RBF network is one approach which has shown a great promise in this sort of problems because of its fast learning algorithm and better approximation capabilities. The adaptive RBF network can effectively improve the control performance against large uncertainty of the system. The adaptive turning laws of network parameters are derived using the back-propagation algorithm and the Lyapunov stability theorem, so that the stability of the entire system and the convergence of the weight adaptation are guaranteed. In this control scheme, a robust compensator plays as an auxiliary controller to guarantee the stability and robustness under various environments such as the mass variation, the external disturbances, and modeling uncertainties. Finally, the simulation and experimental results in comparison with adaptive fuzzy and wavelet network control method are provided to verify the effectiveness of the proposed control methodology.
Adaptive Approaches on the Sliding Mode Control of Robot Manipulators
In this paper, adaptive algorithms on the sliding mode control for robust tracking control of robot manipulators are presented. The presented algorithms use adaptation laws for tuning both the sliding mode gain and the thickness of the boundary layer to reject a discontinuous control input, and to improve the tracking performance. It is shown that the robustness of the developed adaptive algorithms are guaranteed by the sliding mode control law and that the algorithms are globally convergent in the presence of disturbances and modeling uncertainties. Computer simulations are performed for a two-link manipulator, and the results show good properties of the proposed adaptive algorithms under large manipulator parameter uncertainties and disturbances.