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

Guo Pengfei; Liang Zhenying; Wang Xi; Jin Zengke

doi:10.20944/preprints201909.0172.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

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.

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

Computer Science and Mathematics, Applied Mathematics

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

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