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Erschienen in:
2022 | OriginalPaper | Buchkapitel
A VPRNN Model with Fixed-Time Convergence for Time-Varying Nonlinear Equation
verfasst von : Miaomiao Zhang, Edmond Q. Wu
Erschienen in: Intelligent Robotics and Applications
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Abstract
Robots are widely used in various engineering fields, and the solution to their trajectory tracking problem has attracted increasing attention. Such a problem can be typically transformed into a time-varying nonlinear equation (TVNE). For complex and high-precision robot trajectory tracking problems, a fast and low-error tracking solution is necessary. Therefore, a varying-parameter recurrent neural network (VPRNN) model with a modified power-type time-varying parameter is proposed for solving TVNE. An improved sign-bi-power function is selected for the activation function, then the VPRNN model achieves fixed-time convergence. Numerical comparisons with the general fixed-parameter recurrent neural network model are performed, which demonstrates the superiority of our VPRNN model. Besides, the proposed VPRNN model is successfully used to solve the trajectory tracking problem of a three-link robot, which shows its feasibility in practical applications.