Preprint Article Version 1 This version is not peer-reviewed

Fixed-time Stabilization for Uncertain Chained Systems with Sliding Mode and RBF Neural Network

Version 1 : Received: 16 September 2019 / Approved: 16 September 2019 / Online: 16 September 2019 (16:47:55 CEST)

How to cite: Pengfei, G.; Zhenying, L.; Xi, W.; Zengke, J. Fixed-time Stabilization for Uncertain Chained Systems with Sliding Mode and RBF Neural Network. Preprints 2019, 2019090172 (doi: 10.20944/preprints201909.0172.v1). Pengfei, G.; Zhenying, L.; Xi, W.; Zengke, J. Fixed-time Stabilization for Uncertain Chained Systems with Sliding Mode and RBF Neural Network. Preprints 2019, 2019090172 (doi: 10.20944/preprints201909.0172.v1).

Abstract

In this paper, the fixed-time stabilization problem for a class of uncertain chained system is addressed by using a novel nonsingular recursive terminal sliding mode control approach. A fixed-time controller and an adaptive law are designed to guarantee the uncertain chained form system both Lyapunov stable and fixed-time convergent within the settling time. The advantage of the controller based on the sliding mode is that the settling time does not depend on the system initial state. Furthermore, we use RBF neural network to estimate the uncertainty of the system. Finally, the simulation results demonstrate the performance of the control laws.

Subject Areas

Fixed-time stabilization; Sliding mode control; Adaptive control; Neural network

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.