Asymptotic Tracking Control for Uncertain MIMO Nonlinear Systems With Guaranteed Performance and Enhanced Controllability

• 成果类型: Article
• 署名作者: Zhou, Bing; Huang, Xiucai; Song, Yongduan; Lewis, Frank L.
• 署名单位: Chongqing University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3343388
• 发表日期: 2024
• 页码: 4005-4012
• 关键词: MIMO communication; CONTROLLABILITY; Nonlinear systems; transient analysis; Artificial neural networks; steady-state; CONVERGENCE; Enhanced controllability; multi-input multi-output (MIMO) strict-feedback systems; prescribed performance; time-varying control gain

摘要

Most existing control methods for multi-input multi-output (MIMO) uncertain nonlinear systems can only achieve uniformly ultimately bounded stability with conservative controllability condition. In this note, for a larger class of uncertain MIMO strict-feedback nonlinear systems, we present a control solution with enhanced controllability condition by resorting to certain feasible auxiliary matrices, upon which a neural adaptive control scheme is developed that is able to achieve asymptotic tracking with guaranteed preassignable transient and steady-state performance in the presence of mismatched uncertainties and unknown yet time-varying control gain matrices, besides the semiglobal ultimate uniform boundedness of the closed-loop signals. The salient feature of the proposed solution lies in its wider applicability and better control performance. Furthermore, the proposed solution does not involve any filter and the issue of the explosion of complexity is avoided. Numerical simulation also confirms the effectiveness of the proposed method.