我院劉恒教授在《IEEE Transactions on Neural Networks and Learning Systems》上發表學術論文
近日，我院劉恒教授以第一作者在人工智能及計算機領域國際頂級期刊《IEEE Transactions on Neural Networks and Learning Systems》上發表學術論文：Adaptive Neural Network Backstepping Control of Fractional-Order Nonlinear Systems With Actuator Faults, 31(12): 5166-5177, 2020. 該期刊為中科院一區TOP期刊，2020年影響因子為8.792。
Backstepping control for fractional-order nonlinear systems (FONSs) requires the analytic calculation of fractional derivatives of certain complicated stabilizing functions, which becomes prohibitive as the order of the system increases. This article aims to facilitate the adaptive neural network (NN) backstepping control design for FONSs with actuator faults whose parameters and patterns are fully unknown. A fractional filtering approach, which obviates the requirement of analytic fractional differentiation, is used to generate command signals together with their fractional derivatives. Compensated tracking errors that can eliminate approximation errors of command signals are generated by fractional filters. The proposed adaptive NN command filtered backstepping control (ANNCFBC) approach, together with fractional adaptive laws, guarantees not only the boundedness of all involved variables but also the convergence of both the tracking error and the compensated tracking error to a sufficiently small region. Finally, simulation studies are given to indicate the effectiveness of the proposed control method.