This article aims at this problem of adaptive neural tracking control for state-constrained systems. A general fixed-time stability criterion is first presented, by which an adaptive neural control algorithm is developed. Under the action of the proposed adaptive neural tracking controller, the tracking error converges into a small neighborhood around the origin in fixed time; meanwhile, all system states abide by the corresponding state constraints for all the time. The main difference between the present research and the previous control schemes for state-constrained systems is that this article proposes a novel and feasible approach to ensure that the constructed virtual control signals satisfy the state constraints on the corresponding states viewed as the virtual control inputs. Such an approach guarantees theoretically that all the system states cannot violate their constrained requirements at any time. Finally, two simulation examples provide support to the proposed results.

中文翻译：

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具有全状态约束的非线性系统的神经自适应固定时间控制


本文针对状态约束系统的自适应神经跟踪控制问题。首先提出了通用的固定时间稳定性准则，并据此开发了自适应神经控制算法。在所提出的自适应神经跟踪控制器的作用下，跟踪误差在固定时间内收敛到原点周围的小邻域内；同时，所有系统状态始终遵守相应的状态约束。目前的研究与之前的状态约束系统控制方案的主要区别在于，本文提出了一种新颖且可行的方法来确保构造的虚拟控制信号满足被视为虚拟控制输入的相应状态的状态约束。这种方法从理论上保证所有系统状态在任何时候都不能违反其约束要求。最后，两个仿真例子为所提出的结果提供了支持。