RBF neural network sliding mode control of aero-engine distributed control systems

Yu Zhang; Jingbo Peng; Lei Wang; Zhiduo Wang; Xing Wu; Ledi Zhang; Hao Wang

doi:10.1117/12.2652563

10 November 2022 RBF neural network sliding mode control of aero-engine distributed control systems

Yu Zhang, Jingbo Peng, Lei Wang, Zhiduo Wang, Xing Wu, Ledi Zhang, Hao Wang

Proceedings Volume 12331, International Conference on Mechanisms and Robotics (ICMAR 2022); 123310O (2022) https://doi.org/10.1117/12.2652563
Event: International Conference on Mechanisms and Robotics (ICMAR 2022), 2022, Zhuhai, China

Abstract

In this paper, the study of sliding mode control for nonlinear aero-engine distributed control system with time delay and packet loss is carried out. First, the system is modeled as a kind of Markov jump system with unmatched disturbance. Then, a linear sliding mode surface is proposed which is robust to unmatched disturbance. Linear matrix inequality method is used to solve the sliding surface parameter which makes the system mean square asymptotically stable. The RBF neural network is combined with sliding mode control theory in controller designing where the RBF neural network is used to solve the control input that satisfies the sliding mode reaching condition. Simulation results show that the designed controller can make the system stable in a fast time under the conditions of time delay and packet loss.

Citation Download Citation

Yu Zhang, Jingbo Peng, Lei Wang, Zhiduo Wang, Xing Wu, Ledi Zhang, and Hao Wang "RBF neural network sliding mode control of aero-engine distributed control systems", Proc. SPIE 12331, International Conference on Mechanisms and Robotics (ICMAR 2022), 123310O (10 November 2022); https://doi.org/10.1117/12.2652563

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