Abstract
The Lyapunov equation is widely employed in the engineering field to analyze stability of dynamic systems. In this paper, based on a new evolution formula, a novel finite-time recurrent neural network (termed finite-time Zhang neural network, FTZNN) is proposed and studied for solving a nonstationary Lyapunov equation. In comparison with the original Zhang neural network (ZNN) model for a nonstationary Lyapunov equation, the convergence performance has a remarkable improvement for the proposed FTZNN model and can be accelerated to finite time. Besides, by solving the differential inequality, the time upper bound of the FTZNN model is computed theoretically and analytically. Simulations are conducted and compared to validate the superiority of the FTZNN model to the original ZNN model for solving the nonstationary Lyapunov equation. At last, the FTZNN model is successfully applied to online tracking control of a wheeled mobile manipulator.
| Original language | English |
|---|---|
| Article number | 7953678 |
| Pages (from-to) | 98-105 |
| Number of pages | 8 |
| Journal | IEEE Transactions on Industrial Informatics |
| Volume | 14 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Jan 2018 |
Keywords
- Finite-time convergence
- nonstationary Lyapunov equation
- tracking control
- upper bound
- wheeled mobile manipulator
- Zhang neural network (ZNN)
ASJC Scopus subject areas
- Control and Systems Engineering
- Information Systems
- Computer Science Applications
- Electrical and Electronic Engineering