Abstract
The tracking control problem for uncertain spatial robot is investigated by means of adaptive terminal sliding mode control in this article. To approximate unknown nonlinear functions of these systems, a neural network model is employed. By using Lyapunov stability theory, adaptive terminal sliding mode controller is given, which guarantees that the tracking error converges to an arbitrary small region of zero and all the signals remain bounded. Finally, numerical simulation is given to confirm the effectiveness of the proposed method.
Highlights
Robots have been widely used in modern industrial fields, and these applications need to depend on the control of robots that makes it follow the desired trajectory
The robots are complex nonlinear dynamical systems with uncertainties, and it is a difficult task to design the control for these systems
By applying modern control method, the tracking control problem for robot has been studied in the literature.[11,12,13,14,15]
Summary
Robots have been widely used in modern industrial fields, and these applications need to depend on the control of robots that makes it follow the desired trajectory. Keywords Sliding mode control, neural network, spatial robot We usually utilize fuzzy systems or neural networks to approximate unknown nonlinear functions. Based on the neural network learning control methods, convergence for a class of nonlinear systems with functional uncertainties was researched in the study by Pan et al.[32]
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