Adaptive Neural-Fuzzy Sliding-Mode Fault-Tolerant Control for Uncertain Nonlinear Systems Academic Article

abstract

• 2013 IEEE. This paper proposes an adaptive neural-fuzzy sliding-mode control method for uncertain nonlinear systems with actuator effectiveness faults and input saturation. The parameter dependence of the control scheme is removed from the bound of actuator faults by updating online. A neural-fuzzy model is developed to approximate the uncertain nonlinear terms and a sliding-mode online-updating controller is developed to estimate the bound of the actuator with no prior knowledge of the fault. The asymptotic stability is verified via the Lyapunov method in the presence of actuator faults and saturation. Furthermore, the adaptive neural-fuzzy control method is extended to the uncertain faulty nonlinear systems with integral sliding-mode manifold as well as other popular sliding-mode surfaces. A numerical example is presented to demonstrate the effectiveness of the derived results.

authors

• Huang, Tingwen

published proceedings

• IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

author list (cited authors)

• Wen, S., Chen, M., Zeng, Z., Huang, T., & Li, C.

citation count

• 70

complete list of authors

• Wen, Shiping||Chen, Michael ZQ||Zeng, Zhigang||Huang, Tingwen||Li, Chaojie

publication date

• August 2017

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Transactions on Systems, Man, and Cybernetics: Systems  Journal

keywords

• Actuator Fault
• Actuator Saturation
• Neural-fuzzy Approximator
• Sliding-mode Control (smc)
• Uncertain Nonlinear System

Digital Object Identifier (DOI)

• 10.1109/TSMC.2017.2648826

start page

• 2268

end page

• 2278

volume

• 47

issue

• 8

URL

• http://dx.doi.org/10.1109/tsmc.2017.2648826