考虑量化和通信受限的有限时间确定学习控制及其应用

Finite-time deterministic learning control considering quantization and communication constraints with its application

中文关键词  确定学习  量化控制  神经网络  有限时间收敛  事件触发

英文关键词  deterministic learning  quantized control  neural network  finite-time convergence  event-triggered

中文摘要

      本文针对一类含有量化输入和外部扰动的严格反馈非线性系统, 提出了一种考虑量化和通信受限的有限时间确定学习控制方法. 该方法包含离线学习训练和在线触发控制两个阶段. 首先, 在离线学习训练阶段采用神经网络对系统中的未知非线性函数进行逼近, 引入指令滤波反步技术克服“计算爆炸”的问题, 并在控制过程中实现系统未知动态的知识获取和存储. 随后, 利用所获取的经验知识, 设计了基于确定学习机制的在线触发控制器. 应用李雅普诺夫稳定性理论证明了闭环系统实际有限时间稳定, 跟踪误差在有限时间内收敛到原点的邻域内, 并能够排除采样中的芝诺现象. 最后, 通过飞行器仿真验证了所提方案的有效性.

英文摘要

      This paper proposes a finite-time deterministic learning control considering quantization and communication constraints for a class of strictly feedback nonlinear systems with quantized input and external disturbances. The method includes two stages: offline learning training and online triggered control. First, the neural network technique is used to approximate the unknown nonlinear function during the offline learning training stage. The command filter backstepping technique is introduced to overcome the problem of “computational explosion”. As a result, the unknown dynamic knowledge of the system is acquired and stored in the control process. Then, an online triggered controller based on deterministic learning mechanism is designed using the obtained empirical knowledge. The Lyapunov stability theory is employed to prove that the closed-loop system is practically finite-time stable, and the tracking error converges to the neighborhood of the origin in finite time. Finally, the effectiveness of the proposed scheme is verified by aircraft simulation.