Sliding mode control based on RBF neural network for a class of underactuated systems with unknown sensor and actuator faults

Ning Ji, Jinkun Liu and Hongjun Yang

International Journal of Systems Science, 2020, vol. 51, issue 16, 3539-3549

Abstract: A sliding mode control method is developed in this study for application to a class of underactuated systems with bounded unknown disturbance and sensor and actuator faults. In the proposed method, a robustness item compensates for the bounded unknown disturbance and a Nussbaum function realises sensor and actuator faults tolerance simultaneously, and all signals of the system are proven to be bounded. A radial basis function (RBF) neural network is developed to estimate the unknown functions of the system. Finally, Hurwitz stability analysis is conducted to guarantee the stability of the closed-loop system. Simulations are conducted wherein a coupled motor driving system is placed under the proposed control laws to validate this approach.

Date: 2020
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DOI: 10.1080/00207721.2020.1817615

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