【摘要】 研究一般严格反馈型非线性系统的控制问题．假设系统的对象模型、状态均未知,只有输出是可测的．应用自适应模糊神经推断系统辨识对象模型,状态观测器设计为Luenberger型,控制器由反步控制、变结构控制和3层神经网络直接控制综合而成．理论分析和仿真研究都说明此方案能够有效地控制只有输出可测的一般严格反馈型非线性系统．更多还原

【Abstract】 An adaptive control scheme for general nonlinear systems in strict feedback form is addressed in this paper. The strict feedback nonlinear systems are firstly assumed to be with minimum knowledge where only output is measurable. An adaptive neuro-fuzzy inference system is then used to identify the plant. A Luenberger-type observer is also designed to estimate the system states. The controller is a composition of backstepping controller, variable structure controller and three-layer neural networks direct controller. It is proved that the overall control system is capable of controlling a general strict feedback system where system model is unknown. Finally, an example is given to show the effectiveness of the proposed control scheme.更多还原