張曉宇劉彬博
(1.華北科技學(xué)院 電子信息工程學(xué)院,北京 101601; 2. 中央新影集團(tuán) CCTV證券資訊頻道,北京 100080)
模糊、自適應(yīng)等方法在連續(xù)系統(tǒng)控制問(wèn)題中已經(jīng)顯示出成功的運(yùn)用[1-25]。但目前模糊直接、間接自適應(yīng)等方法在離散系統(tǒng)中的運(yùn)用研究尚不深入,有大量待研究、發(fā)展的問(wèn)題。在離散系統(tǒng)中,由于系統(tǒng)的離散化以及系統(tǒng)的理想滑動(dòng)模態(tài)根本不能到達(dá),所以準(zhǔn)滑動(dòng)模態(tài)的降階特性不再存在。這給系統(tǒng)的穩(wěn)定性分析造成了困難?;?刂频姆植皆O(shè)計(jì)法雖然可以實(shí)施,但是不能保證較好的魯棒穩(wěn)定性。離散系統(tǒng)的采樣時(shí)間對(duì)于模糊邏輯系統(tǒng)的逼近能力產(chǎn)生很大影響,對(duì)于自適應(yīng)機(jī)構(gòu)的自適應(yīng)能力、速度也同樣產(chǎn)生較大影響。因而離散系統(tǒng)中模糊自適應(yīng)方法在滑模控制中的應(yīng)用對(duì)于消除滑??刂频亩墩褡饔幂^之連續(xù)系統(tǒng)大大減弱。
本文從自適應(yīng)模糊邏輯系統(tǒng)出發(fā)用離散動(dòng)態(tài)自適應(yīng)模糊邏輯系統(tǒng)逼近滑模控制律,給出逼近誤差收斂的自適應(yīng)機(jī)構(gòu)和構(gòu)建方法;通過(guò)AFLS動(dòng)態(tài)的參數(shù)設(shè)計(jì)形成較好的濾波效果,用以消除抖振。
考慮如下離散非線性系統(tǒng),
(1)
式中:f(xi(k))、g(xi(k))滿足
g(xi(k))=g+Δg(xi(k))
(2)
(3)
設(shè)滑模s(k)=CTe(k),C=[c1c2…cn-11]T∈Rn是Hurwitz多項(xiàng)式的系數(shù)。對(duì)于上述線性不確定離散系統(tǒng)提出以下定理。
定理1 對(duì)于非線性系統(tǒng)(1),若取準(zhǔn)滑??刂坡?/p>
r(k)+(qT1-1)s(k)+k2sgns(k)]
(4)
式中:T1為準(zhǔn)滑模控制器的采樣周期。設(shè)計(jì)參數(shù)q、k2滿足
(5)
證明考慮不等式離散到達(dá)條件
(6)
Δsk+1=sk+1-sk=f(xi(k))+Δgusk-
(qT1-1)sk-k2sgnsk-sk
(qT1-1)sk-k2sgnsk]
(7)
[Δsk+1]2=[f(xi(k))+Δgusk-(qT1-1)sk-k2sgnsk]2+
(8)
若要使得到達(dá)條件(6)滿足,將式(7)、(8)代入式(6)得
(1+Δgg-1)|(qT1-1)sk|+(1+Δgg-1)k2
(9)
依據(jù)式(5)式(9)等價(jià)為
可見(jiàn)在邊界Δ外到達(dá)條件(6)成立。證畢。
定理1雖然得到了系統(tǒng)(3)的一個(gè)SMC,但是這個(gè)SMC使得滑模到達(dá)切換帶是很寬的,而且寬度隨著系統(tǒng)不確定性的變化而變化。這在實(shí)際上會(huì)形成很大的抖振。因此本文尋求其模糊自適應(yīng)SMC以消除抖振。
對(duì)于定理1準(zhǔn)滑??刂坡剩牖_吔鐚訁?shù)λ>0,當(dāng)系統(tǒng)滑模到達(dá)邊界層內(nèi)施加模糊邏輯(FLC)控制律uf(k),停止準(zhǔn)滑??刂坡蕌s(k)。即
u(k)=us(k)+uf(k)
式中:uflc是模糊邏輯系統(tǒng)的輸出,Δ是準(zhǔn)滑動(dòng)模態(tài)區(qū)的寬度,因此λ≥Δ。
邊界層厚度參數(shù)λ的范圍與系統(tǒng)不確定動(dòng)態(tài)非線性函數(shù)的上界有直接關(guān)系。非線性系統(tǒng)的不確定性越大,準(zhǔn)滑動(dòng)模態(tài)區(qū)寬度越大,參數(shù)λ選擇范圍越大。實(shí)驗(yàn)證明λ對(duì)每個(gè)具體非線性系統(tǒng)有一個(gè)最佳值??梢酝ㄟ^(guò)以下方法選擇:已知狀態(tài)的初始值,代入非線性函數(shù)上界得到Δ值。一般λ∈[Δ,3Δ]效果較好。
對(duì)于系統(tǒng)(1)用AFLS來(lái)逼近滑??刂坡伞K捎玫腁FLS與文獻(xiàn)[17]中相同,后件參數(shù)采用梯度優(yōu)化自校正的方法進(jìn)行。
選擇滑模s(k)以及Δs(k)為輸入變量,構(gòu)造具有2個(gè)輸入變量、1個(gè)輸出變量的FBF型AFLS。其中的前件參數(shù)由設(shè)計(jì)者調(diào)節(jié),后件參數(shù)由自適應(yīng)機(jī)構(gòu)校正。模糊控制規(guī)則為
Ri: Ifs(k) issiandΔs(k) isΔsithenuf(j) isθi(j)
i=1,2,…,m
式中:α為滑模s(k)的劃分參數(shù),β為滑模變化率,Δs(k)的劃分參數(shù),j是AFLS的采樣步長(zhǎng),θi(j)是待校正的后件參數(shù),m是規(guī)則總數(shù)。采用單點(diǎn)模糊化、乘積推理、加權(quán)平均解模糊方法滑??刂频哪:平敵鰹?/p>
uf(j)=θ(j)Tp
(10)
定義自適應(yīng)模糊邏輯系統(tǒng)(AFLS)逼近滑??刂坡?4)的誤差目標(biāo)函數(shù)為
這樣AFLS逼近控制律(4)的問(wèn)題就轉(zhuǎn)化為目標(biāo)函數(shù)J(θ,x)的優(yōu)化問(wèn)題。即
求目標(biāo)函數(shù)沿參數(shù)θ方向的梯度得
參數(shù)θ自校正的方向應(yīng)該沿著目標(biāo)函數(shù)對(duì)其梯度的負(fù)方向,由式(11)確定θ的校正方向,
θ(j+1)-θ(j)=p(u(k)-θT(j)p)
(11)
先考慮T1=lT2,l∈Ζ的情況下,AFLS輸出(10)逼近普通離散SMC的誤差。
定理2 對(duì)于準(zhǔn)滑??刂?4),若AFLS以式(11)為自適應(yīng)機(jī)構(gòu)且其采樣周期T2遠(yuǎn)比準(zhǔn)滑??刂?4)的采樣周期T1小,則AFLS(10)逼近準(zhǔn)滑??刂?4)的誤差漸近收斂。
證明在自適應(yīng)律(11)下選取逼近誤差的Lyapunov函數(shù)為
(12)
若AFLS的采樣周期遠(yuǎn)比控制器u(k)的采樣周期小則對(duì)式(12)有
利用式(11)有
pTp(u(k)-θ(j)Tp)θ(j)Tp-pTp(u(k)-θ(j)Tp)·
pTp(u(k)-θ(j)Tp)2
(13)
因?yàn)閜是模糊基函數(shù)向量,所以0 Δv(j)=v(j+1)-v(j)<0 證畢。 接著,為進(jìn)一步加強(qiáng)濾波效果以消除抖振,在AFLS基礎(chǔ)上引入動(dòng)態(tài)自適應(yīng)模糊邏輯系統(tǒng)(dynamic adaptive fuzzy logic system, DAFLS)。 考慮n階連續(xù)DAFLS,則 (14) 式中:di(i=0,1,…,n-1)、γ均為DAFLS的動(dòng)態(tài)參數(shù)。 假設(shè)(14)的動(dòng)態(tài)形成低通濾波。將其離散化得到離散的DAFLS, v(k)[r0+r1z-1+…+rnz-n]=ωθTp (15) 式中:ri(i=0,1,…,n)、ω是導(dǎo)出的參數(shù)。運(yùn)用DAFLS逼近滑??刂剖?4)。 將濾波器(式(14))看作一個(gè)子系統(tǒng),可以通過(guò)選擇狀態(tài)變量,將式(15)變?yōu)闋顟B(tài)空間模型, (16) 式中:θTp看作是子系統(tǒng)(16)的輸入,v(j)是這個(gè)子系統(tǒng)的輸出。定理2已經(jīng)證明如果沒(méi)有引入濾波器動(dòng)態(tài),AFLS逼近滑??刂频恼`差是收斂的。現(xiàn)在選取新的關(guān)于其輸出v(j)與滑??刂浦g誤差的正定Lyapunov函數(shù),其一階差分負(fù)定,則DAFLS的輸出v(j)在適當(dāng)?shù)淖赃m應(yīng)律下逼近滑模控制的誤差仍然收斂。DAFLS中的動(dòng)態(tài)濾波器可以看作是線性系統(tǒng)(16)。 定理3 對(duì)于準(zhǔn)滑??刂?4),若DAFLS(16)以(11)為自適應(yīng)機(jī)構(gòu)且其參數(shù)滿足:ATCTCA半負(fù)定,Cb=1,采樣周期T2遠(yuǎn)比準(zhǔn)滑??刂?4)的采樣周期T1小,則DAFLS(16)逼近準(zhǔn)滑??刂?4)的誤差漸近收斂。 證明選取逼近誤差的Lyapunov函數(shù)為 其一階差分為 ΔV(j)=1/2({[CAη(j)]2+[CbθT(j)p])2- [Cη(j)]2+2CAη(j)CbθT(j)p}+ Cη(j)θT(j)p-θT(j+1)pCAη(j)- θT(j+1)pCbθT(j)p+ 1/2({[θT(j+1)p]2-[θT(j)p]2})+ [θT(j+1)p]2-[θT(j)p]2+ 2us(k)[θ(j)-θ(j+1)]Tp= 1/2({[CAη(j)]2+[CbθT(j)p])2- [Cη(j)]2}+CAη(j)CbθT(j)p+Cη(j)θT(j)p- θT(j+1)pCAη(j)-θT(j+1)pCbθT(j)p+ 2us(k)[θ(j)-θ(j+1)]Tp 若代入θ(j+1)-θ(j)=p(u(k)-θT(j)p),有 [Cη(j)]2}+Cη(j)θT(j)p- [θT(j)p]2Cb-θT(j)pCAη(j)(1-Cb)- pTp(us(k)-θT(j)p)·(CAη(j)+CbθT(j)p)+ 3θT(j)p[us(k)-θT(j)p]pTp- 2us(k)[us(k)-θT(j)p]pTp (17) 若Cb=1,式(17)變?yōu)?/p> 若矩陣A、C滿足Lyapunov方程 ATCTCA-CTC=-Q 其中Q=QT,Q>0。則有[CAη(j)]2≤[Cη(j)]2則 ΔV(j)≤2[(pTp)2-pTp][us(k)-θT(j)p]2 又因?yàn)閜是模糊基向量則pTp≤1 。因此有ΔV(j)≤0成立。若ΔV(j)≡0成立則有 v(j)=us(k)=θT(j)p 成立。根據(jù)Lyapunov理論逼近誤差收斂。證畢。 定理4 對(duì)于準(zhǔn)滑模控制(4),若DAFLS(16)以(11)為自適應(yīng)機(jī)構(gòu)且其參數(shù)且滿足定理2內(nèi)容,則滑模到達(dá)條件(6)能夠得到滿足。 證明考慮不等式離散到達(dá)條件, Δsk+1=sk+1-sk=Δf+Δgufk-(qT1-1)sk- k2sgnsk+g(ufk-usk)-sk (qT1-1)sk-k2sgnsk+g(ufk-usk)] [Δsk+1]2=[Δf+Δgufk-(qT1-1)sk- 2sk[Δf+Δgufk-(qT1-1)sk- k2sgnsk+g(ufk-usk)] 若要使得到達(dá)條件滿足,將skΔsk+1、[Δsk+1]2代入得 (18) 由式(18)可見(jiàn),只要ufk逼近usk誤差為零則與(9)是等同的。證畢。 一階倒立擺系統(tǒng)的離散模型如下: 式中:θ(k)是擺角位移,u(k)是小車控制電壓。 當(dāng)應(yīng)用普通SMC方法控制和應(yīng)用本文提出的控制方法時(shí)得到擺角位移曲線對(duì)比如圖1所示,控制電壓曲線對(duì)比如圖2所示。 圖1 SMC和DAFLSMC控制下擺角位移曲線對(duì)比Fig.1 Angle displacement contract curves of pendulum for SMC and DAFLSMC 圖2 SMC和DAFLSMC控制下電壓曲線對(duì)比Fig.2 Control voltage contract curves for SMC and DAFLSMC 通過(guò)圖1、2可以得出,普通離散SMC穩(wěn)態(tài)抖動(dòng)很大,而本文提出的DAFLSMC消除了抖動(dòng)。比較DAFLSMC控制電壓曲線與在同等條件下實(shí)施普通SMC的控制電壓曲線,如圖2。因?yàn)樵O(shè)計(jì)的DAFLS逼近SMC的采樣時(shí)間是0.01 s而被控對(duì)象及普通SMC采樣時(shí)間是0.1 s。因此DAFLS輸出的控制電壓更加光滑。 為了觀察DAFLS的濾波效果把濾波前后的控制電壓曲線作對(duì)比如圖3所示。從圖3中可見(jiàn)加入了動(dòng)態(tài)濾波的DAFLS后控制信號(hào)的高頻抖動(dòng)部分被濾掉了。 圖3 濾波器前后的控制電壓曲線對(duì)比Fig.3 Control voltage comparison of AFLSMC and DAFLSMC 為驗(yàn)證本文所提出方法的魯棒性能,保持控制器參數(shù)不變的情況下,在仿真t=10 s時(shí),給擺角位移施加幅度為0.3 rad的脈沖干擾,系統(tǒng)控制效果如圖4、圖5所示??梢?jiàn),在較強(qiáng)干擾施加給擺角時(shí),系統(tǒng)仍然能夠穩(wěn)定回到原點(diǎn),穩(wěn)態(tài)性能不變。說(shuō)明本文提出方法的確保留了普通離散SMC的魯棒性。 圖4 DAFLS控制下擺角位移受擾曲線Fig.4 The angle excursion curve when disturbed under DAFLSMC control 圖5 DAFLS控制下受擾時(shí)控制電壓曲線Fig.5 The control volt curve when disturbed under DAFLSMC control 提出了一種離散直接自適應(yīng)模糊滑??刂品椒āMㄟ^(guò)用AFLS逼近離散滑??刂疲薙MC的抖振。逼近SMC的AFLS中必須引入動(dòng)態(tài),才能實(shí)現(xiàn)濾波功能,而且,動(dòng)態(tài)AFLS采樣時(shí)間要比系統(tǒng)采樣時(shí)間小。通過(guò)與普通離散SMC的應(yīng)用仿真對(duì)比,證明了該方法通過(guò)適當(dāng)參數(shù)設(shè)置,不但保證了滑模到達(dá),消除了SMC的抖振,還保留了SMC很強(qiáng)的魯棒性能。該方法對(duì)離散SMC的應(yīng)用具有一定價(jià)值,需進(jìn)一步加強(qiáng)其在實(shí)際控制系統(tǒng)中的應(yīng)用研究。 參考文獻(xiàn): [1]李繼超,管萍,劉小河.間接自適應(yīng)模糊滑??刂圃陔娀t中的應(yīng)用[J].系統(tǒng)仿真學(xué)報(bào), 2009, 21(2): 542-546. LI Jichao, GUAN Ping, LIU Xiaohe. Application of indirect adaptive fuzzy sliding mode control in arc furnace[J]. Journal of System Simulation, 2009, 21(2): 542-546. [2]劉姍梅,馬遷,陳賢順,等.自適應(yīng)模糊滑模控制在PMSM中的應(yīng)用[J].微電機(jī), 2009, 42(5): 43-46, 87. LIU Shanmei, MA Qian, CHEN Xianshun, et al. Application of adaptive fuzzy sliding mode controller in PMSM[J]. Micromotors, 2009, 42(5): 43-46, 87. [3]張向文,王飛躍.汽車ABS自適應(yīng)模糊滑模控制算法研究[J]. 汽車技術(shù), 2009, 40(10): 25-30. ZHANG Xiangwen, WANG Feiyue. Study on adaptive fuzzy sliding mode control algorithm for the vehicle ABS[J]. Automobile Technology, 2009, 40 (10): 25-30. [4]董小閔,余淼,廖昌榮,等. 具有非線性時(shí)滯的汽車磁流變懸架系統(tǒng)自適應(yīng)模糊滑??刂芠J]. 振動(dòng)與沖擊, 2009, 28(11): 55-60, 203. DONG Xiaomin, YU Miao, LIAO Changrong, et al. Adaptive fuzzy sliding mode control for magneto-rheological suspension system considering nonlinearity and time delay[J]. Journal of Vibration and Shock, 2009, 28(11): 55-60, 203. [5]高文達(dá),方一鳴,張文亮,等. 自適應(yīng)模糊滑??刂圃谒欧妱?dòng)機(jī)系統(tǒng)中的應(yīng)用[J]. 微特電機(jī), 2009, 37(11): 32-36. GAO Wenda, FANG Yiming, ZHANG Wenliang, et al. Application of adaptive fuzzy sliding mode control to servomotor system[J]. Micromotors, 2009, 37(11): 32-36. [6]張金萍,劉闊,林劍峰,等.挖掘機(jī)的4自由度自適應(yīng)模糊滑模控制[J].機(jī)械工程學(xué)報(bào), 2010, 46(21): 87-92. ZHANG Jinping, LIU Kuo, LIN Jianfeng, et al. 4-DOF adaptive fuzzy sliding mode control of excavator[J]. Journal of Mechanical Engineering, 2010, 46(21): 87-92. [7]王宏偉,井元偉,于馳.基于自適應(yīng)模糊滑??刂频闹鲃?dòng)隊(duì)列管理算法[J].系統(tǒng)仿真學(xué)報(bào), 2008, 20(23): 6330-6332, 6342. WANG Hongwei, JING Yuanwei, YU Chi. Active queue management algorithm based on adaptive fuzzy sliding mode control[J]. Journal of System Simulation, 2008, 20(23): 6330-6332, 6342. [8]趙紅超,徐君明,王東.變質(zhì)心彈頭的自適應(yīng)模糊滑??刂芠J]. 清華大學(xué)學(xué)報(bào):自然科學(xué)版, 2008, 48(S2): 1733-1736. ZHAO Hongchao, XU Junming, WANG Dong. Adaptive fuzzy sliding mode control for mass motion warhead[J]. Journal of Tsinghua University: Science and Technology, 2008, 48(S2): 1733-1736. [9]彭亞為,陳娟,劉占富, 等. 自適應(yīng)模糊滑??刂圃诨み^(guò)程中的應(yīng)用[J].化工學(xué)報(bào), 2012, 63(9): 2843-2850. PENG Yawei, CHEN Juan, LIU Zhanfu, et al. Adaptive sliding mode control in chemical process application[J]. Journal of Chemical Industry and Engineering, 2012, 63(9): 2843-2850. [10]劉金琨.滑模變結(jié)構(gòu)控制MATLAB仿真[M]. 北京:清華大學(xué)出版社, 2005:32-36. [11]GUO Liping, HUNG J Y, NELMS R M, et al. Comparative evaluation of sliding mode fuzzy controller and PID controller for a boost converter[J]. Electric Power Systems Research, 2011, 81(1): 99-106. [12]POURSAMAD A, DAVAIE-MARKAZI A H. Robust adaptive fuzzy control of unknown chaotic systems[J]. Applied Soft Computing, 2009, 9(3): 970-976. [13]HSU C F, CHUNG I F, LIN C M, et al. Self-regulating fuzzy control for forward DC-DC converters using an 8-bit microcontroller[J]. IET Power Electonics, 2009, 2(1): 1-13. [14]TU K Y, LEE T T, WANG W J. Design of a multi-layer fuzzy logic controller for multi-input multi-output systems[J]. Fuzzy Sets and Systems, 2000, 111(2): 199-214. [15]LIN W S, CHEN C S. Sliding-mode-based direct adaptive fuzzy controller design for a class of uncertain multivariable nonlinear systems[C]//Proceedings of the American Control Conference. Anchorage, Alaska, USA, 2002: 2955-2960. [16]WAI R J, LIN C M, HSU F. Self-organizing fuzzy control for motor-toggle servomechanism via sliding mode technique[J]. Fuzzy Sets and Systems, 2002, 131(2): 235-249. [17]ZHANG X Y, SU H Y, CHU J. Adaptive sliding mode-like fuzzy logic control for high-order nonlinear systems[C]//Proceedings of the 2003 IEEE International Symposium on Intelligent Control. Houston, Texas, USA, 2003: 788-792. [18]HWANG C L, WU H M, SHIH C L, et al. Fuzzy sliding-mode underactuated control for autonomous dynamic balance of an electrical bicycle[J]. IEEE Transactions on Control Systems Technology, 2009, 17(3): 658-670. [19]WANG W, LIU X D. Fuzzy sliding mode control for a class of piezoelectric system with a sliding mode state estimator[J]. Mechatronics, 2010, 20(6): 712-719. [20]HO T H, AHN K. Speed control of a hydraulic pressure coupling drive using an adaptive fuzzy sliding-mode control[J]. IEEE/ASME Transactions on Mechatronics, 2012, 17(5): 976-986. [21]YAU H T, WANG C C, HSIEH C T, et al. Nonlinear analysis and control of the uncertain micro-electro-mechanical system by using a fuzzy sliding mode control design[J]. Computers and Mathematics with Applications, 2011, 61(8):1912-1916. [22]ZHU M C, LI Y C. Decentralized adaptive fuzzy sliding mode control for reconfigurable modular manipulators[J]. International Journal of Robust and Nonlinear Control, 2010, 20(4): 472-488. [23]SHAHRAZ A, BOOZARJOMEHRY R B. A fuzzy sliding mode control approach for nonlinear chemical processes[J]. Control Engineering Practice, 2009, 17(5): 541-550. [24]HO H F, WONG Y K, RAD A B, et al. Adaptive fuzzy sliding mode control with chattering elimination for nonlinear SISO systems[J]. Simulation Modelling Practice and Theory, 2009, 17(7): 1199-1210. [25]PALM R. Sliding mode fuzzy control[C]//Proceedings of IEEE International Conference on Fuzzy Systems. San Diego, CA, USA, 1992: 519-526.3 主要結(jié)果
3.1 動(dòng)態(tài)AFLS的逼近誤差
3.2 近似逼近下的滑??蛇_(dá)性
4 倒立擺系統(tǒng)應(yīng)用仿真
5 結(jié)束語(yǔ)