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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Neural Adaptive Event-Triggered Control for Nonlinear Uncertain Stochastic Systems With Unknown Hysteresis
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Neural Adaptive Event-Triggered Control for Nonlinear Uncertain Stochastic Systems With Unknown Hysteresis

机译：具有未知滞后的非线性不确定随机系统的神经自适应事件触发控制

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摘要

In this paper, the uncertain direct of the hysteretic system component will be considered. Besides, the effect of stochastic disturbance inevitably exists in many practical systems, which would cause the instability. Simultaneously, it is significant to guarantee the perfect error tracking performance for the uncertain nonlinear hysteresis systems when operation suffers the failure. To ensure the maintaining acceptable system performance in reality, the new properties of the Nussbaum function are proposed, and an auxiliary virtual controller is designed through the neural network (NN) universal approximator. Furthermore, it is challenged to save the system-limited transmutation resource for nonlinear systems, especially for stochastic nonlinear systems, with unknown hysteresis input and actuator failures. The coupling effect of the system communication resource constrains has to arise the issue of the mutual coupling function, which makes that the tracking control design is more complicated. Using the proposed event-triggered controller and back-stepping technology, a new optimization algorithm is proposed to ensure that the states of the closed-loop system and the tracking error remain bounded in probability. Finally, to illustrate the effectiveness of our proposed adaptive NN control method with the event-triggered strategy, some numerical examples are provided.
机译：在本文中，将考虑滞后系统分量的不确定方向。此外，在许多实际系统中不可避免地存在随机干扰的影响，这将导致不稳定。同时，对于不确定的非线性磁滞系统，当操作遭受故障时，保证完美的误差跟踪性能也很重要。为了确保在现实中保持可接受的系统性能，提出了Nussbaum函数的新属性，并通过神经网络（NN）通用逼近器设计了辅助虚拟控制器。此外，在滞后输入和执行器故障未知的情况下，为非线性系统（尤其是随机非线性系统）节省系统受限的转换资源也面临着挑战。系统通信资源的耦合效应受到制约，必然会出现相互耦合的问题，使得跟踪控制设计更加复杂。利用提出的事件触发控制器和后推技术，提出了一种新的优化算法，以确保闭环系统的状态和跟踪误差保持在概率范围内。最后，为了说明我们提出的带有事件触发策略的自适应神经网络控制方法的有效性，提供了一些数值示例。

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