Abstract In this article, we present an event-triggered scheme for fractional order nonlinear systems under full-state constraints. The neural networks are employed to approximate the continuous nonlinear unknown functions in the system. Moreover, a new adaptive event-triggered strategy is designed under the unified framework of backstepping control method, which can largely reduce the amount of communications. Since the state constraints are frequently emerged in the control procedure, the barrier Lyapunov functions is used to avoid the violation of state constraints. The stability of the closed-loop system is ensured through fractional Lyapunov stability analysis. Finally, the effectiveness of the proposed scheme is verified by simulation examples.

中文翻译：

---

具有全状态约束的分数阶非线性系统的事件触发自适应神经控制

摘要 在本文中，我们提出了一种在全状态约束下的分数阶非线性系统的事件触发方案。神经网络用于逼近系统中的连续非线性未知函数。此外，在反步控制方法的统一框架下设计了一种新的自适应事件触发策略，可以大大减少通信量。由于状态约束在控制过程中经常出现，所以使用势垒李雅普诺夫函数来避免状态约束的违反。通过分数阶李雅普诺夫稳定性分析确保闭环系统的稳定性。最后，通过仿真算例验证了所提出方案的有效性。