大坝—河流渠道灌溉系统的迭代学习控制  

Iterative Learning Control of Dam-River Channel Irrigation Systems

作　　者：戴喜生 李光[1,3] 周星宇 

机构地区：[1]广西科技大学电气与信息工程学院,广西柳州545006 [2]桂林电子科技大学智能综合自动化高校重点实验室,广西桂林541004 [3]广东工商职业学院智能制造学院,广东肇庆526020

出　　处：《广西师范大学学报（自然科学版）》2018年第1期53-60,共8页Journal of Guangxi Normal University:Natural Science Edition

基　　金：国家自然科学基金(61364006);广西自然科学基金(2017GXNSFAA198179);广西高等学校优秀中青年骨干教师培养工程(桂教人[2014]39号);智能综合自动化高校重点实验室基金(智字201502)

摘　　要：本文考虑大坝—河流渠道灌溉系统的迭代学习控制问题。首先,对线性化的Hayami偏微分方程在空间上离散化,通过叠加向量方法建立闸门与流量关系的状态空间数学模型;其次,结合灌溉过程的重复性特点,设计D型迭代学习控制算法,建立了该算法的收敛性条件。结果表明通过闸门开度的迭代学习控制,可实现对取水口期望流量的完全跟踪。最后,通过数值仿真进一步说明提出的迭代学习控制方法的有效性。The iterative learning control problem of dam-river channel irrigation system is investigated in this paper. Firstly,the Hayami equation of the linearized equation is discretized in space, and the state space mathematical model of the relationship between gate and flow is established by superposition vector method. Secondly, based on the repetitive characteristics of irrigation process, a D-type iterative learning control algorithm is designed, and the convergence condition of the algorithm is also proposed. The results show that the iterative learning control of opening degree of the gate can make the actual output of the system completely track the expected flow rate in the channel. Finally,the validity of the proposed iterative learning control is illustrated by numerical simulation.

关 键 词：迭代学习控制 灌溉系统 Hayami方程 离散化 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]