考虑数据量化的改进无模型自适应迭代学习控制算法  

An improved model free adaptive iterative learning control algorithm with data quantization

作　　者：朱盼盼 卜旭辉[1] 梁嘉琪[1] 闫帅明 ZHU Pan-pan;BU Xu-hui;LIANG Jia-qi;YAN Shuai-ming(School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo Henan 454000,China)

机构地区：[1]河南理工大学电气工程与自动化学院,河南焦作454000

出　　处：《控制理论与应用》2020年第5期1178-1184,共7页Control Theory & Applications

基　　金：国家自然科学基金项目(61573129,61573130,U1804147,61833001);河南省高校科技创新团队项目(20IRTSTHN019);河南理工大学创新型科技团队项目(T2019–2,T2017–1);河南省创新型科技团队项目(CXTD2016054)资助.

摘　　要：针对一类存在数据量化的离散时间单输入单输出非线性系统,提出一种带有编码解码量化机制的无模型自适应迭代学习控制(MFAILC)算法.首先使用伪偏导数将受控非线性系统动态线性化,进而考虑系统输出数据经由均匀量化器进行量化处理的过程,并设计了一种编码解码量化机制,最后基于这种编码解码量化机制提出了一种改进的MFAILC算法.理论上给出了算法的收敛性分析,结果表明,当系统存在数据量化时,所提出的算法仍可保证系统收敛.与已有算法相比,所提算法仅利用较少的输入输出数据,就可以实现跟踪误差的零收敛.仿真进一步验证了算法的有效性.For a class of discrete time single input single output nonlinear systems with data quantization, a model free adaptive iterative learning control(MFAILC) algorithm with encoding and decoding quantization mechanism is proposed.First, the nonlinear system is dynamically linearized by using pseudo partial derivative. Then the output data of the system is quantized by a uniform quantizer, and an encoding and decoding quantization mechanism is designed for the system.Finally, an improved MFAILC algorithm is proposed based on the encoding and decoding quantization mechanism. The convergence of the algorithm is analyzed theoretically. The results show that the proposed algorithm can still guarantee the convergence of the system which is subject to data quantization. Compare with the existing results, the proposed algorithm can only use less input and output data to achieve the zero-error convergence. The simulation validates the effectiveness of the algorithm.

关 键 词：无模型自适应控制 迭代学习 编码解码量化机制 数据量化 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]