文章基本信息

标题：A Novel Robust Adaptive Backstepping Method Combined with SMC on Strict-Feedback Nonlinear Systems Using Neural Networks
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作者：Yahui Li ; Feng Gao ; Franco Bernelli-Zazzera 等
期刊名称：MATEC Web of Conferences
电子版ISSN：2261-236X
出版年度：2019
卷号：291
页码：1-7
DOI：10.1051/matecconf/201929101001
语种：English
出版社：EDP Sciences
摘要：Adaptive backstepping methodology is a powerful tool for nonlinear systems, especially for strict-feedback ones, but its robustness still needs improvements. In this paper, combined with sliding mode control (SMC), a new backstepping design method is proposed to guarantee the robustness. In this method, based on the novel combining method, the auxiliary controller is introduced only in the final step of the real controller, unlike traditional methods, which usually all include an auxiliary controller in every de-signing step to guarantee the robustness of the closed-loop systems. The novel combing methods can avoid calculating multiple and high-order derivatives of the auxiliary controllers in the intermediate steps, low-ering the computational burden in evaluating the controller. The effectiveness of the proposed approach is illustrated from simulation results.