This paper deals with self-triggering adaptive optimal control for nonlinear continuous-time systems. We propose a novel self-triggering control structure concerning a special encoding mechanism, which combines the trigger time of control and sampling and reduces both the control time and the sampling time. Such a triggering structure ensures the existence of a maximum triggering time in self-triggering control. When the system expression is known, the encoding mechanism will lead to high quantitative accuracy at a limited channel transmission rate. Moreover, we also provide a new control algorithm and triggering conditions of the proposed structure. Specifically, this algorithm solves the optimal control strategy by using the cost function approximated by neural networks. Besides, the derived closed-loop system is proven to be asymptotically stable. Finally, two examples are provided to illustrate the effectiveness of the proposed control method.

本文研究非线性连续时间系统的自触发自适应优化控制。我们提出了一种新颖的自触发控制结构，涉及一种特殊的编码机制，它结合了控制和采样的触发时间，减少了控制时间和采样时间。这样的触发结构保证了自触发控制中存在最大触发时间。当系统表达式已知时，编码机制将在有限的信道传输速率下导致较高的定量精度。此外，我们还提供了一种新的控制算法和所提出结构的触发条件。具体来说，该算法利用神经网络近似的代价函数求解最优控制策略。除了，推导出的闭环系统被证明是渐近稳定的。最后，提供了两个例子来说明所提出的控制方法的有效性。