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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Neural Network-Based Event-Triggered State Feedback Control of Nonlinear Continuous-Time Systems
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Neural Network-Based Event-Triggered State Feedback Control of Nonlinear Continuous-Time Systems

机译：基于神经网络的非线性连续时间事件触发状态反馈控制

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摘要

This paper presents a novel approximation-based event-triggered control of multi-input multi-output uncertain nonlinear continuous-time systems in affine form. The controller is approximated using a linearly parameterized neural network (NN) in the context of event-based sampling. After revisiting the NN approximation property in the context of event-based sampling, an event-triggered condition is proposed using the Lyapunov technique to reduce the network resource utilization and to generate the required number of events for the NN approximation. In addition, a novel weight update law for aperiodic tuning of the NN weights at triggered instants is proposed to relax the knowledge of complete system dynamics and to reduce the computation when compared with the traditional NN-based control. Nonetheless, a nonzero positive lower bound for the inter-event times is guaranteed to avoid the accumulation of events or Zeno behavior. For analyzing the stability, the event-triggered system is modeled as a nonlinear impulsive dynamical system and the Lyapunov technique is used to show local ultimate boundedness of all signals. Furthermore, in order to overcome the unnecessary triggered events when the system states are inside the ultimate bound, a dead-zone operator is used to reset the event-trigger errors to zero. Finally, the analytical design is substantiated with numerical results.
机译：本文提出一种新型的基于仿射的多输入多输出不确定非线性连续时间系统的事件触发控制。在基于事件的采样中，使用线性参数化神经网络（NN）来近似控制器。在基于事件的采样中重新审视NN近似属性后，使用Lyapunov技术提出了一个事件触发条件，以降低网络资源利用率并生成NN近似所需的事件数。此外，提出了一种新颖的权重更新定律，用于非常规地调整触发时刻的NN权重，从而放宽了完整系统动力学的知识并与传统的基于NN的控制相比减少了计算量。尽管如此，确保事件间时间的非零正下限可以避免事件或Zeno行为的累积。为了分析稳定性，将事件触发系统建模为非线性脉冲动力系统，并使用Lyapunov技术显示所有信号的局部最终有界性。此外，为了克服系统状态在极限范围内时不必要的触发事件，使用死区运算符将事件触发错误重置为零。最后，分析设计得到了数值结果的证实。

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