An orthogonal ARX network for identification and control of nonlinear systems

机译：用于非线性系统识别和控制的正交ARX网络

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摘要

This paper presents a new orthogonal neural network (ONN) which is utilized successively for online identification and control of nonlinear discrete-time systems. The proposed network is designed with auto regressive with exogenous (ARX) terms of inputs and outputs, and their orthogonal terms by Chebyshev polynomials. The network is a single layer neural network and computationally efficient with less number of parameters. The identification by the network is performed in stable sense by using Lyapunov stability guaranteed learning rate. Hence, the learning rate depends on the current knowledge of the system instead of using constant learning rate. This learning rate provides fine online optimization. In simulation study, one benchmark nonlinear system is identified and results are compared. Then, one nonlinear functioned system is identified and controlled by model reference control. From results, it is seen that the proposed model has good learning capability for identification and control.

机译：本文提出了一种新的正交神经网络（ONN），该网络被连续用于非线性离散时间系统的在线识别和控制。拟议的网络是根据输入和输出的外生（ARX）项以及Chebyshev多项式的正交项进行自回归设计的。该网络是单层神经网络，计算效率高，参数数量少。通过使用Lyapunov稳定性保证的学习率，可以稳定地进行网络识别。因此，学习率取决于系统的当前知识，而不是使用恒定的学习率。此学习率可提供良好的在线优化。在仿真研究中，确定了一个基准非线性系统并进行了比较。然后，通过模型参考控制来识别和控制一个非线性功能系统。从结果可以看出，该模型具有良好的识别和控制学习能力。

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来源
《Information, Communication and Automation Technologies, 2009. ICAT 2009》|2009年|1-5|共5页
会议地点 Sarajevo(BA);Sarajevo(BA)
作者
Beyhan S.; Alci M.;
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作者单位

Dept. of Electr. Electron. Eng., Ege Univ., Izmir, Turkey;

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Chebyshev approximation; Lyapunov methods; discrete time systems; identification; learning systems; model reference adaptive control systems; neural nets; nonlinear systems; polynomial approximation; regression analysis; Chebyshev polynomial; Lyapunov stability; auto regressive method; exogenous term; fine online optimization; learning rate; model reference control; nonlinear discrete time system; orthogonal ARX network; orthogonal neural network; system current knowledge; systems identification; ARX Model; ONN; Stable;

机译：Chebyshev逼近; Lyapunov方法;离散时间系统;识别;学习系统;模型参考自适应控制系统;神经网络;非线性系统;多项式逼近;回归分析; Chebyshev多项式; Lyapunov稳定性;自回归方法;外生项;精细在线优化;学习率模型参考控制非线性离散时间系统正交ARX网络正交神经网络系统当前知识系统辨识ARX模型ONN稳定;

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专利

1. NARX-Based Nonlinear System Identification Using Orthogonal Least Squares Basis Hunting [J] . Chen S., Wang X. X., Harris C. J. IEEE Transactions on Control Systems Technology . 2008,第1期

机译：基于NARX的非线性最小二乘基础狩猎的非线性系统识别
2. Hierarchical Algorithms of Quasi-Linear ARX Neural Networks for Identification of Nonlinear Systems [J] . Mohammad Abu Jamiin, Yuyun, Eko Julianto Engineering Letters . 2017,第3期

机译：非线性系统辨识的拟线性ARX神经网络的分层算法。
3. NARX model based nonlinear dynamic system identification using low complexity neural networks and robust H_∞ filter [J] . H. K. Sahoo, P. K. Dash, N. P. Rath Applied Soft Computing . 2013,第7期

机译：低复杂度神经网络和鲁棒H_∞滤波器的基于NARX模型的非线性动态系统辨识
4. An Orthogonal ARX Network for Identification and Control of Nonlinear Systems [C] . Selami Beyhan, Musa Alci International Symposium on Information, Communication and Automation Technologies . 2009

机译：用于识别和控制非线性系统的正交ARX网络
5. Nonlinear system identification and control using fast orthogonal search. [D] . Eklund, J. Mikael. 2003

机译：使用快速正交搜索的非线性系统识别和控制。
6. Improved Delay-Dependent Stability Conditions for MIMO Networked Control Systems with Nonlinear Perturbations [O] . Jiuwen Cao -1

机译：具有非线性摄动的MIMO网络控制系统的改进的时延相关稳定性条件
7. NARX-based nonlinear system identification using orthogonal least squares basis hunting [O] . Chen, S., Wang, X.X., Harris, C.J. 2008

机译：基于NaRX的正交最小二乘搜索非线性系统辨识

An orthogonal ARX network for identification and control of nonlinear systems

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