PID补偿的完全在线序贯极限学习机控制器在输入扰动系统自适应控制中的应用

doi:10.11772/j.issn.1001-9081.2017092207

计算机应用 ›› 2018, Vol. 38 ›› Issue (4): 1213-1217.DOI: 10.11772/j.issn.1001-9081.2017092207

• 应用前沿、交叉与综合 • 上一篇下一篇

PID补偿的完全在线序贯极限学习机控制器在输入扰动系统自适应控制中的应用

张立优¹, 马珺¹, 贾华宇²

1. 太原理工大学物理与光电工程学院, 山西晋中 030600;
2. 太原理工大学信息工程学院, 山西晋中 030600

收稿日期:2017-09-11 修回日期:2017-10-26 出版日期:2018-04-10 发布日期:2018-04-09
通讯作者: 张立优
作者简介:张立优(1990-),男,山西大同人,硕士研究生,主要研究方向:智能检测与控制;马珺(1980-),女,山西太原人,副教授,博士,主要研究方向:智能控制、新型传感器;贾华宇(1977-),男,山西临汾人,副教授,博士,主要研究方向:自动控制、控制电路设计。
基金资助:
山西省自然科学基金资助项目（2015011050）。

Application of fully online sequential extreme learning machine controller with PID compensation in input-disturbance system adaptive control

ZHANG Liyou¹, MA Jun¹, JIA Huayu²

1. College of Physics and Optoelectronics, Taiyuan University of Technology, Jinzhong Shanxi 030600, China;
2. College of Information Engineering, Taiyuan University of Technology, Jinzhong Shanxi 030600, China

Received:2017-09-11 Revised:2017-10-26 Online:2018-04-10 Published:2018-04-09
Supported by:
This work is partially supported by the Natural Science Foundation of Shanxi Province (2015011050).

摘要/Abstract

摘要： 针对输入受外界扰动的系统在实现自适应控制难的问题，提出一种比例-积分-微分（PID）补偿的完全在线序贯极限学习机（FOS-ELM）控制器设计方法。首先，建立系统的动态线性模型，采用FOS-ELM算法设计控制器并学习其参数；其次，计算系统的实际输出误差，结合系统的控制误差，设计所需补偿的PID增量参数；最后，对PID补偿的FOS-ELM控制器参数在线调整并用于系统控制。在发动机空气燃油比（AFR）控制系统模型上进行实验，实验结果表明上述方法在实现自适应控制的同时降低了系统扰动输入带来的干扰，提高了系统有效控制率，在正负干扰系数为0.2时，其有效控制率从不足53%提高到93%以上。同时该方法易于实现，具有很强的鲁棒性和实用价值。

关键词: 完全在线序贯极限学习机, 输入扰动, 自适应控制, 比例-积分-微分增量, 控制误差

Abstract: To deal with the difficulty of input disturbance system in achieving adaptive control, a design method for the Fully Online Sequential Extreme Learning Machine (FOS-ELM) controller with Proportion-Integral-Derivative (PID) compensation was proposed. Firstly, a dynamic linear model of the system was established, then the FOS-ELM algorithm was used to design the controller and learn its parameters. Secondly, by calculating the output error of the system and combining with the system control error, the PID parameters of the system compensation were designed. Finally, the FOS-ELM controller parameters for PID compensation were adjusted online and used for system control. The experiment was carried out on engine Air Fuel Ratio (AFR) control system. The results show that the proposed method can achieve the adaptive control, reduce the disturbance caused by system disturbance input, and obviously improve the effective control rate of the system at the same time. When the positive and negative interference coefficients are 0.2, the effective control rate is increased from less than 53% to over 93%. In addtion, the proposed method is easy to implement and has strong robustness and practical value.

Key words: Fully Online Sequential Extreme Learning Machine (FOS-ELM), input disturbance, adaptive control, Proportion-Integral-Derivative (PID) increment, control error

中图分类号:

TP391.8

张立优, 马珺, 贾华宇. PID补偿的完全在线序贯极限学习机控制器在输入扰动系统自适应控制中的应用[J]. 计算机应用, 2018, 38(4): 1213-1217.

ZHANG Liyou, MA Jun, JIA Huayu. Application of fully online sequential extreme learning machine controller with PID compensation in input-disturbance system adaptive control[J]. Journal of Computer Applications, 2018, 38(4): 1213-1217.

参考文献

[1] CHEN F C. Back-propagation neural networks for nonlinear self-tuning adaptive control[J]. IEEE Control Systems Magazine, 1990, 10(3):44-48.
[2] WONG P K, VONG C M, GAO X H, et al. Adaptive control using fully online sequential-extreme learning machine and a case study on engine air-fuel ratio regulation[J]. Mathematical Problems in Engineering, 2014, 2014:Article ID 246964.
[3] LIU Y J, CHEN C L P, WEN G X, et al. Adaptive neural output feedback tracking control for a class of uncertain discrete-time nonlinear systems[J]. IEEE Transactions on Neural Networks, 2011, 22(7):1162-1167.
[4] LECUN Y A, BOTTOU L, ORR G B, et al. Efficient back prop[M]//Neural networks:Tricks of the Trade. Berlin:Springer, 2012:9-48.
[5] 李敏远, 都延丽. 基于遗传算法学习的复合神经网络自适应温度控制系统[J]. 控制理论与应用, 2004, 21(2):242-246.(LI M Y, DU Y L. Composite neural networks adaptive control system of temperature based on GA learning[J]. Control Theory & Applications, 2004, 21(2):242-246.)
[6] HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine:a new learning scheme of feedforward neural networks[C]//Proceedings of the 2004 IEEE International Joint Conference on Neural Networks. Piscataway, NJ:IEEE, 2004, 2:985-990.
[7] HUANG G B, WANG D H, LAN Y. Extreme learning machines:a survey[J]. International Journal of Machine Learning and Cybernetics, 2011, 2(2):107-122.
[8] LIANG N Y, HUANG G B, SARATCHANDRAN P, et al. A fast and accurate online sequential learning algorithm for feedforward networks[J]. IEEE Transactions on Neural Networks, 2006, 17(6):1411-1423.
[9] HUYNH H T, WON Y. Regularized online sequential learning algorithm for single-hidden layer feedforward neural networks[J]. Pattern Recognition Letters, 2011, 32(14):1930-1935.
[10] LI X L, JIA C, LIU D, et al. Adaptive control of nonlinear discrete-time systems by using OS-ELM neural networks[J]. Abstract and Applied Analysis, 2014, 2014:Article ID 267609.
[11] DENG W Y, ZHENG Q H, CHEN L. Regularized extreme learning machine[C]//CIDM 2009:Proceedings of the 2009 IEEE Symposium on Computational Intelligence and Data Mining. Piscataway, NJ:IEEE, 2009:389-395.
[12] 邓万宇, 郑庆华, 陈琳, 等. 神经网络极速学习方法研究[J]. 计算机学报, 2010, 33(2):279-287.(DENG W Y, ZHENG Q H, CHEN L, et al. Research on extreme learning of neural networks[J]. Chinese Journal of Computers, 2010, 33(2):279-287.)
[13] JIA C, LI X, WANG K, et al. Adaptive control of nonlinear system using online error minimum neural networks[J]. ISA Transactions, 2016, 65:125-132.

PID补偿的完全在线序贯极限学习机控制器在输入扰动系统自适应控制中的应用

Application of fully online sequential extreme learning machine controller with PID compensation in input-disturbance system adaptive control

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