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. Preprints2019, 2019090172. https://doi.org/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. https://doi.org/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. Preprints2019, 2019090172. https://doi.org/10.20944/preprints201909.0172.v1
APA Style
Pengfei, G., Zhenying, L., Xi, W., & Zengke, J. (2019). Fixed-time Stabilization for Uncertain Chained Systems with Sliding Mode and RBF Neural Network. Preprints. https://doi.org/10.20944/preprints201909.0172.v1
Chicago/Turabian Style
Pengfei, G., Wang Xi and Jin Zengke. 2019 "Fixed-time Stabilization for Uncertain Chained Systems with Sliding Mode and RBF Neural Network" Preprints. https://doi.org/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.
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.