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

作　　者：王冠 夏红伟[1] WANG Guan;XIA Hong-wei(School of Astronautics,Harbin Institute of Technology,Harbin Heilongjiang 150001,China)

机构地区：[1]哈尔滨工业大学航天学院,黑龙江哈尔滨150001

出　　处：《控制理论与应用》2024年第4期649-657,共9页Control Theory & Applications

基　　金：国家自然科学基金项目(61304108);国家重点研发计划项目(2020YFC2200600)资助.

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.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.

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 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]