3872 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 56, NO. 10, OCTOBER 2009
PID-Like Neural Network Nonlinear Adaptive
Control for Uncertain Multivariable
Motion Control Systems
S. Cong and Y. Liang
Abstract—A mix locally recurrent neural network was used
to create a proportional-integral-derivative (PID)-like neural net-
work nonlinear adaptive controller for uncertain multivariable
single-input/multi-output system. It is composed of a neural net-
work with no more than three neural nodes in hidden layer, and
there are included an activation feedback and an output feedback,
respectively, in a hidden layer. Such a special structure makes the
exterior feature of the neural network controller able to become a
P, PI, PD, or PID controller as needed. The closed-loop error be-
tween directly measured output and expected value of the system
is chosen to be the input of the controller. Only a group of initial
weights values, which can run the controlled closed-loop system
stably, are required to be determined. The proposed controller can
update weights of the neural network online according to errors
caused by uncertain factors of system such as modeling error and
external disturbance, based on stable learning rate. The resilient
back-propagation algorithm with sign instead of the gradient is
used to update the network weights. The basic ideas, techniques,
and system stability proof were presented in detail. Finally, actual
experiments both of single and double inverted pendulums were
implemented, and the comparison of effectiveness between the
proposed controller and the linear optimal regulator were given.
Index Terms—Neural network, nonlinear adaptive control,
proportional-integral-derivative (PID), single-input/multi-output
(SIMO), uncertain multivariable system.
I. INTRODUCTION
I
N industry applications, proportional-integral-derivative
(PID) control is a very popular control strategy due to its
simple architecture and easy tuning. Despite their widespread
use and considerable history, PID tuning is still an active area
of research, both academic and industrial. During the past five
decades, a comprehensive PID tuning literature has been de-
veloped. Roughly speaking, there are two different approaches
to obtain PID and PID-like controller parameters. First, tune
the parameters of the PID structure by following one of sev-
eral available tuning techniques: Ziegler–Nichol method [1],
internal-model-control-based method [2], optimization method
[3], and gain-phase margin method [4]. For single-input/single-
Manuscript received May 8, 2008; revised October 7, 2008, December 12,
2008, and January 29, 2009. First published April 7, 2009; current version
published September 16, 2009. This work was supported by the National
Science Foundation of China under Grant 60774098.
The authors are with the Department of Automation, University of Science
and Technology of China, Hefei 230027, China (e-mail: scong@ustc.edu.cn;
sunnylg@mail.ustc.edu.cn).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TIE.2009.2018433
output (SISO) plants, satisfactory control can be achieved by
using established tuning rules. These rules can be applied to
multivariable plants with SISO characteristics as well. Many
multivariable plants, particularly the single-input/multi-output
(SIMO) plants, however, show significant internal interaction.
Since the tuning rules are developed for SISO system, their
applications to such “true” multivariable plants are ineffective.
Furthermore, in the case of multivariable plants, the number of
PID parameters becomes quite a lot. Trial and error techniques
are thus inadequate to derive a good compromise between
controller performance and robustness. Second, assume that the
controller has a PID structure, and find the PID parameters by
using some well-known optimization methods, e.g., H
∞
[5],
mixed H
2
/H
∞
[6], and semidefinite programming approaches
[7]. These methods can be used to obtain the PID controller
parameters such that the controllers have good time-domain
performance and frequency-domain robustness. The main prob-
lem with this approach is that the resulting controllers are state-
space controllers of high-order rather than low-order controllers
with a fixed structure. Although one can reduce or approximate
it with a PID-like structure, it is not so far the reduced-order
controller.
With advantages like the abilities of adaptive, parallel, and
fault tolerance, artificial neural networks plays an important
role in many fields. From the end of the 1980s, introductions
to neural network control are successively given. Feedforward
controllers in which the neural network learns to mimic the
inverse of the plant are intensively discussed by [8]–[11].
Currently, many different neural network control methods are
proposed which include the adaptive neural networks to model
the plant [12], the analytical redundancy method using neural
network modeling of the induction motor in vibration spectra
for machine fault detection and diagnosis [13], the method of
in direct adaptive control using neural network model of the
process and its inverse [14], and adaptive controller with neural
network uncertain compensation [15].
In the real world, the system parameters are often time
varying, so the conventional PID and PID-like control scheme
becomes not flexible in dealing with the s ystems in which
there exist uncertain factors such as modeling error and external
disturbance. In addition, some modern control approaches are
also proposed, but the design and analysis procedures are
complex and difficult. From these factors, the specifications
required for real applications of control theories are that the
control structures and algorithms should be simple enough to be
0278-0046/$26.00 © 2009 IEEE
Authorized licensed use limited to: University of Science and Technology of China. Downloaded on October 9, 2009 at 05:10 from IEEE Xplore. Restrictions apply.
下载后可阅读完整内容，剩余7页未读，立即下载
weixin_38564085
- 粉丝: 5
- 资源: 937
我的内容管理
展开
最新资源
- 多功能HTML网站模板：手机电脑适配与前端源码
- echarts实战：构建多组与堆叠条形图可视化模板
- openEuler 22.03 LTS专用openssh rpm包安装指南
- H992响应式前端网页模板源码包
- Golang标准库深度解析与实践方案
- C语言版本gRPC框架支持多语言开发教程
- H397响应式前端网站模板源码下载
- 资产配置方案：优化资源与风险管理的关键计划
- PHP宾馆管理系统（毕设）完整项目源码下载
- 中小企业电子发票应用与管理解决方案
- 多设备自适应网页源码模板下载
- 移动端H5模板源码，自适应响应式网页设计
- 探索轻量级可定制软件框架及其Http服务器特性
- Python网站爬虫代码资源压缩包
- iOS App唯一标识符获取方案的策略与实施
- 百度地图SDK2.7开发的找厕所应用源代码分享
资源上传下载、课程学习等过程中有任何疑问或建议，欢迎提出宝贵意见哦~我们会及时处理！
点击此处反馈
