增益自适应滑模控制器在微型飞行器飞行姿态控制中的应用

李迪; 陈向坚; 续志军

doi:10.3788/OPE.20132105.1183

您当前的位置：

首页 >

文章列表页 >

增益自适应滑模控制器在微型飞行器飞行姿态控制中的应用

微纳技术与精密机械 | 更新时间：2020-08-12

- 增益自适应滑模控制器在微型飞行器飞行姿态控制中的应用
- Gain adaptive sliding mode controller used for flight attitude control of MAV
- 光学精密工程 2013年21卷第5期页码：1183-1191
- 作者机构：
  
  中国科学院长春光学精密机械及物理研究所,吉林长春,130033
- 作者简介：
- 基金信息：
  
  中国国家自然科学基金();吉林省自然科学基金()
- DOI：10.3788/OPE.20132105.1183
  中图分类号： TP273.2;V448.22
- 收稿日期：2012-02-16，
  
  修回日期：2012-04-12，
  
  网络出版日期：2013-05-24，
  
  纸质出版日期：2013-05-15
- 稿件说明：
移动端阅览
李迪陈向坚续志军. 增益自适应滑模控制器在微型飞行器飞行姿态控制中的应用[J]. 光学精密工程, 2013,21(5): 1183-1191

LI Di CHEN Xiang-jian XU Zhi-jun. Gain adaptive sliding mode controller used for flight attitude control of MAV[J]. Editorial Office of Optics and Precision Engineering, 2013,21(5): 1183-1191
李迪陈向坚续志军. 增益自适应滑模控制器在微型飞行器飞行姿态控制中的应用[J]. 光学精密工程, 2013,21(5): 1183-1191 DOI： 10.3788/OPE.20132105.1183.

LI Di CHEN Xiang-jian XU Zhi-jun. Gain adaptive sliding mode controller used for flight attitude control of MAV[J]. Editorial Office of Optics and Precision Engineering, 2013,21(5): 1183-1191 DOI： 10.3788/OPE.20132105.1183.

摘要

针对微型飞行器的姿态角摄动引起的系统不确定性及外界干扰等问题，提出了基于区间二型模糊神经网络辨识的增益自适应模糊控制器。首先，给出了微型飞行器姿态动力学模型。然后，采用区间二型模糊神经网络对滑模控制器中由于姿态角摄动引起的系统不确定性进行在线辨识，通过增益自适应滑模控制器中的校正控制项对辨识误差及负载干扰进行补偿。最后，通过设计李亚普诺夫函数，得到闭环系统一致稳定条件下的区间二型模糊神经网络参数在线调整的自适应律及滑模增益自适应律。仿真对比表明，与传统的增益自适应滑模控制器和基于一型模糊神经网络辨识的滑模控制器及相比，本文提出的控制器不仅对系统的不确定性因素及外界干扰具有较强的鲁棒性，而且稳定误差小，跟踪精度高。

Abstract

A gain adaptive sliding mode controller based on interval type-II fuzzy neural network identification was proposed to handle the system uncertainty and the external disturbances come from the attitude angle disturbance of a Micro Aircraft Vehicle (MAV). Firstly

the attitude dynamical model of MAV was established. Then

the interval type-II fuzzy neural network was used to approximate the nonlinearity function and uncertainty functions in the attitude angle dynamic model of the MAV. The correct items from the gain adaptive sliding mode controller were taken to compensate identification errors and load disturbances. Finally

Lyapunov stability theorem was designed and the adaptive law and the sliding mode gain adaptive law for adjusting on line interval type-II fuzzy neural network parameters were obtained under the condition of asymptotic stability of the closed-loop system. The numerical simulation and comparison were performed and the results show that the proposed control system has not only stronger robustness to system uncertainty and external disturbances but also more excellent steady characteristics and tracking accuracy as compared with the conventional adaptive sliding model controller and the type-I fuzzy neural network based sliding mode controller.

关键词

Keywords

references

李占科,宋笔锋,宋海龙.微型飞行器的研究现状及其关键技术\[J\]. 飞行力学,2003,21(4):1-4.LI ZH K,SONG B F,SONG H L. Study on actualities of micro air vehicles and its key technology \[J\]. Fiight Dynamics, 2003,21(4):1-4. (in Chinese)[2]翁梓华,黄太平,吴金明,等.微型飞行器的研究进展和主要技术\[J\]. 航空制造技术, 2005(2):98-102.WENG X H,HUANG T P,WU J M, et al.. Research progress and key technologies of mciro air vehicle \[J\]. Aeronautical Manufacturing Technology, 2005, (2):98-102. (in Chinese) [3]CHENG M B, VERICA R, SU W CH. Slding model boundary control of a parabolic PDE system with parameter variations and boundary uncertainties \[J\]. Automatica, 2011,47(2):381-387.[4]LIU X, WANG W. High order sliding mode and its application on the tracking control of piezoelectric systems \[J\]. Int J Innovative Comput Inform Control, 2008， 4:697-704.[5]张玲瑄,贾振元,任小涛,等.微细电火花加工放电状态逐级映射检测\[J\]. 光学精密工程, 2010, 18(3):663-669.ZHANG L X,JIA ZH Y,REN X T, et al.. Successive mapping detection of micro EDM discharge state \[J\]. Opt. Precision Eng., 2010, 18(3):663-669. (in Chinese) [6]段洪君.微型飞行器飞行姿态控制方法研究\[D\].哈尔滨：哈尔滨工业大学,2007.DUAN H J.Flight Attitude control of MAV \[D\].Harbin:Harbin Institute of Technology,2007. (in Chinese)[7]陈向坚,李迪,白越,等.模糊神经网络在自适应双轴运动控制系统中的应用\[J\]. 光学精密工程, 2011,19(8):1643-1650.CHEN X J,LI D,BAI Y,et al.. Application of type-II fuzzy neural network to adaptive double axis motion control system \[J\]. Opt. Precision Eng., 2011,19(8): 1643-1650. (in Chinese)[8]李迪,陈向坚,续志军,等.自组织递归区间二型模糊神经网络在动态时变系统辨识中的应用\[J\]. 光学精密工程, 2011,19(6):1406-1413.LI D,CHEN X J,XU ZH J, et al.. Type-II fuzzy neural networks with self-organizing recurrent intervals for dynamic time-varying system identification \[J\]. Opt. Precision Eng., 2011,19(6):1406-1413. (in Chinese)[9]CHEN X J, LI D, XU ZH J, et al.. Robust control of quadrotor MAV using self-organizing interval type-II fuzzy neural networks (SOIT-IIFNNs) controller \[J\].International Journal of Intelligent Computing and Cybernetics, 2011, 4(3):397-412.[10]陈向坚,李迪,续志军,等.四旋翼微型飞行器的区间二型模糊神经网络自适应控制\[J\]. 光学精密工程, 2012,20(6):1334-1341.CHEN X J,LI D,XU ZH J, et al.. Adaptive control of quadrotor MAV using interval type-II fuzzy neural network \[J\]. Opt. Precision Eng., 2012,20(6):1334-1341. (in Chinese)[11]CHEN X J, LI D, BAI Y, et al.. Modeling and Neuro-Fuzzy adaptive attitude control for Eight-Rotor MAV \[J\]. International Journal of Control, Automation and Systems, 2011, 9(6):1154-1163.[12]CHEN X K. Adaptive sliding mode control for discrete-time multi-input multi-output systems \[J\]. Automatica, 2006, 42(3):427-435.[13]HUNG L C, CHUNG H Y. Decoupled sliding-mode with fuzzy-neural network controller for nonlinear systems \[J\]. International Journal of Approximate Reasoning, 2007,46:74-97.

浏览量

112

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

低轨光学卫星同轨立体成像姿态规划与控制方法

阿斯图微卫星姿态控制系统设计

双框架控制力矩陀螺框架系统的扰动观测及抑制

分布式遥感编队多星协同观测中的姿态控制

微动量轮高精度滑模变结构控制