Abstract
In this article, a robust adaptive tracking controller is developed for robot manipulators with uncertain dynamics using radial basis function neural network. The design of tracking control systems for robot manipulators is a highly challenging task due to external disturbance and the uncertainties in their dynamics. The improved radial basis function neural network is chosen to approximate the uncertain dynamics of robot manipulators and learn the upper bound of the uncertainty. The adaptive law based on the Lyapunov stability theory is used to solve the uniform final bounded problem of the radial basis function neural network weights, which guarantees the stability and the consistent bounded tracking error of the closed-loop system. Finally, the simulation results are provided to demonstrate the practicability and effectiveness of the proposed method.
Highlights
With the development of robot technologies, the tracking control of robot manipulators becomes the focus of research during recent years
An improved RBFNN was constructed firstly by modifying the clustering radius with variable step to learn the uncertain dynamics of the system online, robust controller is designed to compensate for the uncertainty, and the stability and tracking performance are guaranteed
An improved RBFNN adaptive control method was proposed to eliminate the effects of uncertain dynamics
Summary
With the development of robot technologies, the tracking control of robot manipulators becomes the focus of research during recent years. Keywords Robot manipulators, neural network, adaptive, uncertain dynamics, tracking control In the study of Wang,[13] two different adaptive control strategies were proposed to realize the satisfied trajectory tracking performance under the uncertain dynamics and kinematics functions.
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