The issue of adaptive tracking control for nonlinear uncertain system with self-triggered input and immeasurable states is investigated. The Radial Basis Function of Neural Networks (RBFNNs) is introduced to approximate unknown nonlinear functions. Based on this, an observer is constructed to estimate the immeasurable states. Considering that the self-triggered input may cause poor tracking performance, the prescribed performance bound method is combined into co-designing. Furthermore, a self-triggered adaptive control scheme is presented to guarantee close-loop system signals are bounded. Compared with the event-triggered scheme, the presented self-triggered scheme can compute the next trigger point through the current one. It is not required to continuously monitor the measurement error to determine whether the triggering condition is reached. By the given control scheme, the tracking error can be bounded in prescribing performance bounds, and reaches the balance of network resource saving and tracking performance. These are verified by the given simulation example.

研究了具有自触发输入和不可测状态的非线性不确定系统的自适应跟踪控制问题。引入了神经网络的径向基函数 (RBFNN) 来逼近未知的非线性函数。在此基础上，构造一个观察器来估计不可测状态。考虑到自触发输入可能导致较差的跟踪性能，将规定的性能约束方法结合到协同设计中。此外，提出了一种自触发自适应控制方案，以保证闭环系统信号是有界的。与事件触发方案相比，本文提出的自触发方案可以通过当前触发点计算下一个触发点。不需要持续监测测量误差以确定是否达到触发条件。通过给定的控制方案，跟踪误差可以在规定的性能范围内得到限制，达到网络资源节约和跟踪性能的平衡。这些已通过给定的仿真示例进行验证。