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• W3205729836 abstract "Summary This article investigates an adaptive neural network (NN) output‐feedback optimal control design problem for active suspension systems (ASSs) with stochastic disturbance. The ASSs under consideration contain the characteristics of spring nonlinear dynamics, unmeasured states, and state constraints. The NNs are developed to approximate the unknown nonlinear functions. Meanwhile, observer‐based output feedback control design method is proposed based on the adaptive backstepping technique. Furthermore, the stability of the closed‐loop system is demonstrated by constructing the barrier Lyapunov function, thus ensuring that the full‐state constraints are not exceeded. In particular, the simulation validations are given for the cases of bump, C‐class, and D‐class road displacements inputs. Finally, the simulation results verify the effectiveness of the studied control strategy." @default.
• W3205729836 created "2021-10-25" @default.
• W3205729836 creator A5060259824 @default.
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• W3205729836 date "2021-10-05" @default.
• W3205729836 modified "2023-10-10" @default.
• W3205729836 title "Neuro‐adaptive output‐feedback optimized stochastic control for the active suspension systems with state constraints" @default.
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• W3205729836 doi "https://doi.org/10.1002/acs.3339" @default.
• W3205729836 hasPublicationYear "2021" @default.
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