Neuroadaptive Output-Feedback Tracking Control for Stochastic Nonlower Triangular Nonlinear Systems With Dead-Zone Input
针对带有死区输入和不可测状态的非下三角随机非线性系统，提出了一种结合状态观测器和动态面技术的神经自适应跟踪控制方法，并通过仿真验证了其有效性。
For stochastic nonlower triangular nonlinear systems subject to dead-zone input, a neuroadaptive tracking control frame is constructed by applying the dynamic surface technique with a state observer in this work. Its primary contribution lies in extending the stability criteria to encompass stochastic nonlinear systems characterized by nonlower triangular structures and unmeasured states. The control strategy is delineated as follows. First, the state observer is designed to address the issue of unmeasured states, thereby facilitating the generation of an error dynamics system for subsequent analysis. Second, within the backstepping design framework, a neural network-based tracking controller is devised using dynamic surface control technique and variable separation approaches, ensuring system performance despite the presence of unmeasured states. Finally, stability analysis is conducted to guarantee that all the system signals remain bounded. Simulation examples are presented to illustrate the validity and practicality of the framework.