Article(id=1205158464629748537, tenantId=1146029695717560320, journalId=1189987059142926344, issueId=1205158458619306387, articleNumber=null, orderNo=null, doi=10.19457/j.1001-2095.dqcd24798, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1668960000000, receivedDateStr=2022-11-21, revisedDate=1676908800000, revisedDateStr=2023-02-21, acceptedDate=null, acceptedDateStr=null, onlineDate=1765261768728, onlineDateStr=2025-12-09, pubDate=1721404800000, pubDateStr=2024-07-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1765261768728, onlineIssueDateStr=2025-12-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1765261768728, creator=13701087609, updateTime=1765261768728, updator=13701087609, issue=Issue{id=1205158458619306387, tenantId=1146029695717560320, journalId=1189987059142926344, year='2024', volume='54', issue='7', pageStart='3', pageEnd='96', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1765261767296, creator=13701087609, updateTime=1765261938922, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1205159178575782323, tenantId=1146029695717560320, journalId=1189987059142926344, issueId=1205158458619306387, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1205159178575782324, tenantId=1146029695717560320, journalId=1189987059142926344, issueId=1205158458619306387, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=11, endPage=15, ext={EN=ArticleExt(id=1205158464898184006, articleId=1205158464629748537, tenantId=1146029695717560320, journalId=1189987059142926344, language=EN, title=Chaos Research of H-bridge Inverter Based on Active Disturbance Rejection Control, columnId=null, journalTitle=Electric Drive, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To solve the chaos problem of inverter in new energy generation system,a regulation scheme of H-bridge inverter based on active disturbance rejection control(ADRC) was proposed. Aiming at the phenomenon of poor steady-state and dynamic characteristics of inverters in chaotic state,a discrete model of the inverter was developed to control the chaos by establishing tracking differentiators,extended state observers,and state error feedback control laws. Through bifurcation diagram,folding diagram and simulation,the results show that the stability range of the H-bridge inverter based on ADRC control is expanded by 45% compared with the H-bridge inverter based on proportional control,which effectively suppresses the occurrence of chaos. It can be concluded that the control strategy can restrain the chaotic behavior of the system and broaden the stable working range of the system.

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为了解决新能源发电系统逆变器出现混沌的问题,提出了一种基于自抗扰控制的H桥逆变器的调节方案。针对处于混沌状态的逆变器的稳态和动态特性差的现象,构建了逆变器的离散模型,并通过建立跟踪微分器、扩张状态观测器以及状态误差反馈控制律进行混沌控制。通过分岔图、折叠图以及仿真分析,结果可知基于自抗扰控制的H桥逆变器比基于比例控制的H桥逆变器的稳定范围扩大了45%,有效地抑制了混沌现象的发生。通过分析可得出:该控制策略对系统的混沌行为能够进行抑制,拓宽了系统稳定工作范围。

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何晓宇 (1996—),男,硕士研究生,研究方向为电力系统分析与控制,Email:
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马幼捷(1964—),女,博士,教授,研究方向为电力系统分析与控制,Email:

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马幼捷(1964—),女,博士,教授,研究方向为电力系统分析与控制,Email:

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基于自抗扰控制的H桥逆变器的混沌研究
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马幼捷 1, 2 , 何晓宇 1, 2 , 周雪松 1, 2 , 张超 1, 2
电气传动 | 电力电子 2024,54(7): 11-15
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电气传动 | 电力电子 2024, 54(7): 11-15
基于自抗扰控制的H桥逆变器的混沌研究
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马幼捷1, 2 , 何晓宇1, 2 , 周雪松1, 2, 张超1, 2
作者信息
  • 1 天津理工大学 电气工程与自动化学院,天津 300384
  • 2 天津理工大学 天津市复杂系统控制理论与应用重点实验室,天津 300384
  • 马幼捷(1964—),女,博士,教授,研究方向为电力系统分析与控制,Email:

通讯作者:

何晓宇 (1996—),男,硕士研究生,研究方向为电力系统分析与控制,Email:
Chaos Research of H-bridge Inverter Based on Active Disturbance Rejection Control
Youjie MA1, 2 , Xiaoyu HE1, 2 , Xuesong ZHOU1, 2, Chao ZHANG1, 2
Affiliations
  • 1 School of Electrical Engineering and Automation, Tianjin University of Technology,Tianjin 300384,China
  • 2 Tianjin Key Laboratory of Control Theory and Application for Complex Systems,Tianjin University of Technology, Tianjin 300384, China
出版时间: 2024-07-20 doi: 10.19457/j.1001-2095.dqcd24798
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为了解决新能源发电系统逆变器出现混沌的问题,提出了一种基于自抗扰控制的H桥逆变器的调节方案。针对处于混沌状态的逆变器的稳态和动态特性差的现象,构建了逆变器的离散模型,并通过建立跟踪微分器、扩张状态观测器以及状态误差反馈控制律进行混沌控制。通过分岔图、折叠图以及仿真分析,结果可知基于自抗扰控制的H桥逆变器比基于比例控制的H桥逆变器的稳定范围扩大了45%,有效地抑制了混沌现象的发生。通过分析可得出:该控制策略对系统的混沌行为能够进行抑制,拓宽了系统稳定工作范围。

自抗扰控制  /  H桥逆变器  /  离散模型  /  混沌  /  分岔图

To solve the chaos problem of inverter in new energy generation system,a regulation scheme of H-bridge inverter based on active disturbance rejection control(ADRC) was proposed. Aiming at the phenomenon of poor steady-state and dynamic characteristics of inverters in chaotic state,a discrete model of the inverter was developed to control the chaos by establishing tracking differentiators,extended state observers,and state error feedback control laws. Through bifurcation diagram,folding diagram and simulation,the results show that the stability range of the H-bridge inverter based on ADRC control is expanded by 45% compared with the H-bridge inverter based on proportional control,which effectively suppresses the occurrence of chaos. It can be concluded that the control strategy can restrain the chaotic behavior of the system and broaden the stable working range of the system.

active disturbance rejection control(ADRC)  /  H-bridge inverter  /  discrete models  /  chaos  /  bifurcation diagram
马幼捷, 何晓宇, 周雪松, 张超. 基于自抗扰控制的H桥逆变器的混沌研究. 电气传动, 2024 , 54 (7) : 11 -15 . DOI: 10.19457/j.1001-2095.dqcd24798
Youjie MA, Xiaoyu HE, Xuesong ZHOU, Chao ZHANG. Chaos Research of H-bridge Inverter Based on Active Disturbance Rejection Control[J]. Electric Drive, 2024 , 54 (7) : 11 -15 . DOI: 10.19457/j.1001-2095.dqcd24798
如今新能源发电不断发展,在智能电网的快速建设中,逆变器成为变换器中重要的组成部件。但是由于此系统中存有大量的开关器件,在其开关过程中会使其成为一种较强的非线性系统,表现出很多的复杂非线性行为,比如分岔和混沌等现象[1]。因此,研究逆变器的非线性行为并设计控制方式保证其稳定状态,或者避开不稳定状态,是有一定意义的。
对于逆变器的混沌研究,已有学者取得了一些成果。文献[2-3]通过研究单相H桥逆变器的非线性现象,提出了离散映射模型。文献[4-5]通过电压控制单相H桥逆变器的不稳定现象,发现了逆变器中的分岔现象。文献[6]通过单相H桥逆变器的离散映射模型,求出其系统的稳定范围。文献[7]对单相H桥逆变器进行分析并作出其分岔图和李亚普诺夫指数谱,通过对图形的分析,发现其非线性区域。文献[8-10]通过延迟反馈法(time delayed feedback control,TDFC)对逆变器进行控制。文献[11]通过扩展时延反馈控制(extend time delayed feedback control,ETDFC)控制桥逆变器的混沌行为,这两种方法鲁棒性强、易于构造。文献[12]采用斜坡补偿法控制峰值电流,改善了该逆变器的各项性能指标。文献[13]采用Washout滤波器法,有效控制了系统的混沌行为。
近年来,出现了一种对于扰动有很强的耐受性的控制策略——自抗扰控制(active disturbance rejection control,ADRC )。本文通过ADRC调节逆变器的混沌,首先构建系统离散映射模型;然后通过建立跟踪微分器(tracking differentiator,TD)、扩张状态观测器(extended state observer,ESO)以及状态误差反馈控制律(linear state error feedback,LSEF),控制所需的反馈信号,最后通过分岔图和折叠图以及仿真分析,可以得出,ADRC控制有效抑制了系统中的混沌行为,并可以扩大系统的稳定工作范围。
H桥逆变器电路图如图1所示,其中 i r e f为参考电流。通过电流反馈控制,当开关频率远远大于调制信号的频率时,参考电流在一个开关周期内为恒定值。
当S1,S3闭合,S2,S4断开时:
d i L d t = - 1 L u C - 1 L E d u C d t = - 1 R C u C + 1 C i L
当S2,S4闭合,S1,S3断开时:
d i L d t = - 1 L u C + 1 L E d u C d t = - 1 R C u C + 1 C i L
用频闪映射理论对系统进行离散化,可得逆变器的迭代方程为
x n + 1 = ( I + A T S ) x n + [ I + A ( 1 - d n ) T S · d n T S B 1 + ( 1 - d n ) T S · d n T S B 2 ] E
其中
$\left\{\begin{array}{l} \boldsymbol{A}=\left[\begin{array}{cc} 0 & -1 / L \\ 1 / C & -1 / R C \end{array}\right] \\ \boldsymbol{B}_{1}=\left[\begin{array}{c} 1 / L \\ 0 \end{array}\right] \\ \boldsymbol{B}_{2}=\left[\begin{array}{c} -1 / L \\ 0 \end{array}\right] \end{array}\right.$
$d_{n}=\left\{\begin{array}{ll} 0 & d_{n} \leqslant 0 \\ D+k\left(i_{\text {refn }}-i_{n}\right) & 0<d_{n}<1 \\ 1 & d_{n} \geqslant 0 \end{array}\right.$
式中:D为稳态工作时开关占空比,为一常量;k为比例调节系数; i r e f n为正弦参考电流在第n个开关周期的采样值,当开关频率远远大于调制信号的频率时,参考电流在一个开关周期内为恒定值。
则比例调节下的单相H桥逆变器的离散模型为式(3)和式(5)。
ADRC控制器主要由三个部分组成:TD,ESO以及状态误差反馈控制律(nonlinear state error feedback,NLSEF),三者相互作用,提高了系统的鲁棒性和动态性能。控制框图如图2所示。
在实际工程中,信号输入在其传递的过程中会受到一些干扰,因此设计TD环节,可以起到平滑信号突变的作用,合理地提取理想输入信号,进而克服噪声信号造成的影响。于是能够得到如下所示的表达式:
f   h = f h a n [ v 1 ( k - 1 ) - r , v 2 ( k - 1 ) ,   ρ , h 0 ] v 1 ( k ) = v 1 ( k - 1 ) + h 0 v 2 ( k - 1 ) v 2 ( k ) = v 2 ( k - 1 ) + h 0 f   h
b 0 = v 1 ( k - 1 ) - r + h 0 v 2 ( k - 1 ) b 1 = h 0 2 ρ 2 + 8 r b 0 b 2 = v 2 ( k ) + b 1 - h 0 ρ 2 s i g n ( b 0 )         b 0 > h 0 2 ρ v 2 ( k ) + b 0 h 0                                                         b 0 h 0 2 ρ f h a n = - ρ s i g n ( b 2 )                                                     b 2 > h 0 ρ b 2 h 0                                                                       b 2 h 0 ρ
其中 r = i r e f
式中:r为系统的理想输入信号; ρ为快速因子; h 0为滤波因子,其值与采样步长 h相关联。
v 1 ( k )跟踪的是 i r e f v 2 ( k )相当于信号 i r e f微分项的近似,而 f   h相当于 r ¨的近似值。函数 f h a n中的参数 h 0的选取应适当大于步长 h,并且其值与滤波作用成正比,增大 h 0是增强滤波效果的重要策略。同时,积分步长值与其增益能力成反比,选取适当大小的步长有助于抑制噪声; ρ值与滤波效果成正比,因此当步长确定时,为了得到更好的滤波效果,可以增大 ρ
考虑到系统中存在频率波动、线路损耗等一系列不确定性以及实际系统的不理想性,设计如下所示的观测器:
e ( k ) = z 1 ( k - 1 ) - i ( k ) z 1 ( k ) = z 1 ( k - 1 ) + h { z 2 ( k - 1 ) -                               β 01 f a l [ e ( k ) , a 1 , δ 1 ] } z 2 ( k ) = z 2 ( k - 1 ) + h { z 3 ( k - 1 ) -                               β 02 f a l [ e ( k ) , a 1 , δ 1 ] + h b 0 u ( k - 1 ) } z 3 ( k ) = z 3 ( k - 1 ) + h β 03 f a l [ e ( k ) , a 1 , δ 1 ]
f a l ( x , a , δ ) = x δ ( 1 - a )             | x | δ s i g n ( x ) | x | a | x | > δ
ESO中参数 a 1 δ 1 β 01 β 02 β 03需要整定。 a 1在0~1之间取得, a 1取值较小时,其非线性行为强,使系统的扰动能力增强,一般 a 1取0.2。对于参数 δ 1,表征的是非线性函数的线性区间宽度,取值大时非线性会转化为线性反馈,但取值过小又会出现高频脉动,最后取 δ 1为6。参数 β 01 β 02 β 03的取值对于系统也有很大的影响, β 01过大会带来振荡甚至发散; β 02过大会带来发散,过小会产生高频噪声; β 03影响对扰动的估计,过大会产生振荡,过小会降低跟踪速度。通过Matlab仿真进行参数整定,最后代入系统实验后分别取 β 01 β 02 β 03为100,300和1 500。
状态误差反馈控制律主要是通过状态误差求理论控制量,表达式如下:
e 1 ( k ) = v 1 ( k ) - z 1 ( k ) e 2 ( k ) = v 2 ( k ) - z 2 ( k ) u 0 ( k ) = k p f a l [ e ( k ) , a 2 , δ 2 ] + k d f a l [ e ( k ) , a 2 , δ 2 ] u ( k ) = u 0 ( k ) - z 3 ( k ) / b 0
其中,参数 a 2 δ 2取值与 a 1 δ 1一样,分别为0.5和5; k p为比例增益, k p增大,会减少误差,但是会降低快速性,实验整定后取450; k d为微分增益, k d增大,增加快速性,但是过大会产生振荡,通过多次测试后取20; b 0的取值跟ESO中补偿量有关, b 0的取值不同使得其扰动的范围不同,当 b 0取得值大的时候,可以补偿系统的扰动和模型中不确定的因素,可以提高抗扰能力,最后通过测试取为5。
基于ADRC控制单相H桥逆变器混沌化的实现过程如图3所示,取控制的信号为输出电流 i n,令其为输入信号,将ADRC的输出信号作用于系统的比例系数k,以达到对系统混沌化控制的目的。 d n可表示为
d n = 0                               d n 0 D + k u         0 < d n < 1 1                                 d n 0
利用Matlab/Simulink进行程序的编写和搭建模型并进行仿真。H桥逆变器中数据参数如下:直流电压源 E = 350   V,负载电阻 R = 30   Ω,负载电感 L = 30   m H,负载电容 C = 12   μ F,占空比 D = 0.4,开关频率 f s = 5   k H z f = 50   H z i r e f = 5   s i n 2 f π t )
通过Matlab编写分岔图和折叠图的程序。图4图5为比例控制和ADRC控制时 k p变化的分岔图;图6图7为比例控制和ADRC控制时 k p = 0.8的折叠图。
图4可以看出, k p的稳定范围为0~0.42,系统在 k p为0.43~0.52时进入分岔,在 k p大于0.53时系统进入混沌状态。由图5可以看出, k p的稳定范围为0~0.91,在 k p大于0.92时,系统进入混沌状态。由分岔图可以看出采用ADRC控制可以提高稳定范围,扩大约45%。
图6可以看出比例控制 k p = 0.8时,H桥逆变器的电流输出呈现出多个不定值,此时系统是不稳定的混沌状态;由图7可以看出ADRC控制 k p = 0.8时,H桥逆变器的电流输出是一条稳定的、单一的正弦曲线,此时系统是稳定的单周期稳定状态。故采用ADRC扩大了H桥逆变器的稳定范围。
通过Matlab/Simulink对H桥逆变器系统搭建模型仿真,仿真结果如图8~图13所示。图8为比例控制 k p = 0.8的H桥逆变器仿真图,图9为ADRC控制 k p = 0.8的H桥逆变器仿真图。
图8图9可以看出,比例控制在 k p = 0.8时,系统处于混沌的状态;但是ADRC控制在 k p = 0.8时,系统是稳定状态。图10图11为系统加入扰动后,系统的状态仿真图。图8图10对比可以看出比例调节控制H桥逆变器加入扰动后,系统状态更加复杂。由于自抗扰控制自身具有抗扰能力,对比图9图11可以看出采用ADRC控制后,系统变化不大。故可以证明ADRC能够扩大稳定范围和抗扰能力。
图12图13 k p = 0.3时,比例控制和ADRC控制时 E变化的H桥逆变器的分岔图。由图12分析可得,比例控制时 E在0~445 V时,系统是稳定的; E在445~470 V时,系统运行在分岔范围; E>470 V时,系统进入混沌状态。由图13可知,ADRC控制H桥逆变器 E在0~610 V时,系统是稳定的; E>610 V时,系统进入分岔状态,然后进入混沌状态。由图12图13可得,采用ADRC控制H桥逆变器,可以使其稳定范围扩大,扩大约40%。
本文针对单相逆变器的非线性动力学行为,提出一种基于ADRC的H桥逆变器的混沌控制策略,介绍了该方案的基本原理,并与比例控制进行仿真模拟实验对比。综上所述,该控制是在比例控制的基础上加入了一个ADRC,理论分析和控制仿真结果表明,在加入ADRC后系统稳定范围扩大;自抗扰控制对于扰动有一定的抗扰能力,故采用ADRC控制H桥逆变器提高了系统的抗干扰性能。
  • 国家自然科学基金面上项目(51877152)
  • 天津市自然科学基金(18JCZDJC97300)
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doi: 10.19457/j.1001-2095.dqcd24798
  • 接收时间:2022-11-21
  • 首发时间:2025-12-09
  • 出版时间:2024-07-20
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  • 收稿日期:2022-11-21
  • 修回日期:2023-02-21
基金
国家自然科学基金面上项目(51877152)
天津市自然科学基金(18JCZDJC97300)
作者信息
    1 天津理工大学 电气工程与自动化学院,天津 300384
    2 天津理工大学 天津市复杂系统控制理论与应用重点实验室,天津 300384

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何晓宇 (1996—),男,硕士研究生,研究方向为电力系统分析与控制,Email:
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