齊曉靜 劉文慧
摘要
本文研究了具有量化輸入信號(hào)和未知擾動(dòng)的非線性系統(tǒng)的有限時(shí)間自適應(yīng)輸出反饋動(dòng)態(tài)面控制問題.在控制設(shè)計(jì)過程中,利用模糊邏輯系統(tǒng)對(duì)系統(tǒng)中的非線性項(xiàng)進(jìn)行逼近.然后引入一種滯回量化器來避免量化信號(hào)中的抖振,并且構(gòu)造模糊觀測(cè)器來估計(jì)系統(tǒng)中不可測(cè)的狀態(tài).為了提出一種有限時(shí)間控制策略,首先給出了半全局實(shí)際有限時(shí)間穩(wěn)定的判據(jù).在此基礎(chǔ)上,將動(dòng)態(tài)面控制技術(shù)與反步法相結(jié)合,設(shè)計(jì)了自適應(yīng)模糊控制器.該控制器不僅能保證跟蹤誤差在有限時(shí)間內(nèi)收斂到原點(diǎn)的一個(gè)小鄰域,而且可以保證閉環(huán)系統(tǒng)中所有信號(hào)的有界性.最后通過一個(gè)仿真實(shí)例驗(yàn)證了該控制方法的有效性和可行性.關(guān)鍵詞
量化輸入信號(hào);模糊邏輯系統(tǒng);動(dòng)態(tài)面控制;反步法;有限時(shí)間跟蹤控制
中圖分類號(hào) TP273.4
文獻(xiàn)標(biāo)志碼 A
0 引言
在過去的幾十年里,自適應(yīng)控制方法作為求解參數(shù)不確定的非線性系統(tǒng)控制問題的主要方法之一得到了廣泛的應(yīng)用[1-6].此外,為了克服復(fù)雜未知的非線性函數(shù)對(duì)非線性系統(tǒng)的影響,利用模糊邏輯系統(tǒng)(FLSs)[7]或神經(jīng)網(wǎng)絡(luò)(NNs)[8],提出了許多針對(duì)不確定非線性系統(tǒng)的模糊或神經(jīng)網(wǎng)絡(luò)控制方法[9-11].近年來,將自適應(yīng)控制方法與模糊邏輯系統(tǒng)或神經(jīng)網(wǎng)絡(luò)相結(jié)合,取得了許多有意義的研究成果.比如,文獻(xiàn)[12-14]針對(duì)不確定的嚴(yán)格反饋非線性系統(tǒng),構(gòu)造了基于FLSs或NNs的自適應(yīng)控制方法.在文獻(xiàn)[15-16]中,基于FLSs或NNs,提出了非線性純反饋系統(tǒng)的自適應(yīng)控制方法.文獻(xiàn)[17-18]研究了非線性非嚴(yán)格反饋系統(tǒng)的自適應(yīng)智能控制問題.在已有方法的基礎(chǔ)上,本文采用模糊自適應(yīng)控制方法,提出了一種有限時(shí)間自適應(yīng)模糊控制器.
目前,量化控制已經(jīng)成為控制工程中的一個(gè)重要課題.它已經(jīng)廣泛應(yīng)用于數(shù)字控制系統(tǒng)、混合系統(tǒng)和網(wǎng)絡(luò)控制系統(tǒng)中,如文獻(xiàn)[19-21]研究了非線性系統(tǒng)的量化控制問題.這些系統(tǒng)的一個(gè)共同特點(diǎn)是需要通過組件之間的無線媒體傳輸信息.由于無線通信網(wǎng)絡(luò)的物理局限性,所以引入了量化技術(shù)來降低通信速率.設(shè)計(jì)量化控制系統(tǒng)的控制方案,其基礎(chǔ)問題是保證系統(tǒng)在低帶寬下能夠正常運(yùn)行.因此量化對(duì)于許多實(shí)際控制系統(tǒng)是必要的,也是有益的.本文采用滯后量化器來消除文獻(xiàn)[22]中提出的對(duì)數(shù)量化器所引起的抖振現(xiàn)象.
值得注意的是,在傳統(tǒng)的反步技術(shù)中,由于某些非線性函數(shù)在每一步的重復(fù)微分會(huì)導(dǎo)致“復(fù)雜性爆炸”.因此,為了避免這一問題,提出了動(dòng)態(tài)表面控制(DSC)技術(shù).該方法將一階濾波器引入到反步法的每一步中,將原微分運(yùn)算轉(zhuǎn)化為代數(shù)運(yùn)算,使得在實(shí)際中難以控制的模型易于實(shí)現(xiàn).近幾十年來,動(dòng)態(tài)表面控制技術(shù)在不確定非線性系統(tǒng)的自適應(yīng)控制中得到了廣泛的應(yīng)用.例如,文獻(xiàn)[23-25]針對(duì)嚴(yán)格反饋或純反饋非線性系統(tǒng),研究了基于FLSs或NNs的自適應(yīng)動(dòng)態(tài)表面控制策略.文獻(xiàn)[26-28]研究了基于動(dòng)態(tài)面技術(shù)的非嚴(yán)格反饋非線性系統(tǒng)的自適應(yīng)控制方法.雖然上述文獻(xiàn)所設(shè)計(jì)的控制器可以保證閉環(huán)系統(tǒng)的有界性,但不能保證系統(tǒng)在有限時(shí)間內(nèi)的穩(wěn)定性. 因此,本文將研究閉環(huán)系統(tǒng)的有限時(shí)間穩(wěn)定性.
上述研究問題主要與無限時(shí)間跟蹤控制有關(guān).然而,在實(shí)際工程中,控制目標(biāo)往往需要在有限的時(shí)間內(nèi)收斂.有限時(shí)間控制可以使閉環(huán)系統(tǒng)具有更快的響應(yīng)速度、更高的跟蹤精度和更好的抗干擾能力.因此,近年來有限時(shí)間控制的分析與綜合越來越受到人們的重視.例如,文獻(xiàn)[29]針對(duì)一類具有輸入飽和的非線性嚴(yán)格反饋系統(tǒng),提出了一種模糊自適應(yīng)有限時(shí)間控制設(shè)計(jì)方法;文獻(xiàn)[30]研究了非線性純反饋系統(tǒng)的自適應(yīng)有限時(shí)間跟蹤控制方法.隨后,文獻(xiàn)[31-32]設(shè)計(jì)了狀態(tài)觀測(cè)器,消除了文獻(xiàn)[29-30]中要求狀態(tài)完全可測(cè)量的限制,提出了有限時(shí)間自適應(yīng)控制策略.然而,如何有效地解決具有輸入量化和未知擾動(dòng)的非線性系統(tǒng)的有限時(shí)間自適應(yīng)控制問題仍是一個(gè)棘手的問題.
本文的主要貢獻(xiàn)如下:
1) 針對(duì)一類具有量化輸入和未知擾動(dòng)的非線性系統(tǒng),提出了一種新的自適應(yīng)控制方案.與文獻(xiàn)[5]和文獻(xiàn)[16]相比,本文不僅考慮了系統(tǒng)的量化輸入和未知擾動(dòng),而且提出了一種有限時(shí)間自適應(yīng)模糊控制策略.
2) 本文提出了一種輸出反饋控制方案,設(shè)計(jì)了模糊自適應(yīng)觀測(cè)器來估計(jì)系統(tǒng)中的不可測(cè)狀態(tài).并且,本文采用滯回量化器對(duì)輸入信號(hào)進(jìn)行量化,避免了量化信號(hào)中的抖振.
3) 本文采用動(dòng)態(tài)面控制技術(shù),克服了反步設(shè)計(jì)中“復(fù)雜性爆炸”的缺點(diǎn),降低了控制算法的計(jì)算復(fù)雜度.
5 結(jié)束語
本文針對(duì)一類具有量化輸入和未知擾動(dòng)的非線性系統(tǒng),提出了一種基于輸出反饋的有限時(shí)間自適應(yīng)模糊控制方案.采用模糊邏輯系統(tǒng)對(duì)系統(tǒng)中的非線性項(xiàng)進(jìn)行逼近,利用一種滯回量化器來避免量化信號(hào)中的抖振.該控制方案可保證整個(gè)系統(tǒng)是SGPFS,保證閉環(huán)系統(tǒng)中的所有信號(hào)都是有界的,并且觀測(cè)器誤差及跟蹤誤差能夠在有限時(shí)間內(nèi)收斂到原點(diǎn)的一個(gè)小鄰域,可以獲得良好的跟蹤性能.仿真結(jié)果驗(yàn)證了該控制方法的有效性和可行性.
參考文獻(xiàn)
References
[1]
李剛,苗國英,張靜怡.基于觀測(cè)器的多智能體系統(tǒng)的自適應(yīng)一致性控制[J].南京信息工程大學(xué)學(xué)報(bào)(自然科學(xué)版),2019,11(4):373-379
LI Gang,MIAO Guoying,ZHANG Jingyi.Observer-based adaptive consensus control for multi-agent systems[J].Journal of Nanjing University of Information Science & Technology (Natural Science Edition),2019,11(4):373-379
[2] 高志峰,韓冰,錢默抒,等.考慮傳感器故障的柔性航天器自適應(yīng)積分滑模主動(dòng)容錯(cuò)控制[J].南京信息工程大學(xué)學(xué)報(bào)(自然科學(xué)版),2018,10(2):146-152
GAO Zhifeng,HAN Bing,QIAN Moshu,et al.Active fault tolerant control for flexible spacecraft with sensor faults based on adaptive integral sliding mode[J].Journal of Nanjing University of Information Science & Technology (Natural Science Edition),2018,10(2):146-152
[3] Li Y M,Tong S C.Adaptive fuzzy output-feedback control of pure-feedback uncertain nonlinear systems with unknown dead zone[J].IEEE Transactions on Fuzzy Systems,2014,22(5):1341-1347
[4] Zhou Q,Wang L,Wu C,et al.Adaptive fuzzy control for nonstrict-feedback systems with input saturation and output constraint[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2017,47(1):1-12
[5] Esfandiari K,Abdollahi F,Talebi H A.Adaptive control of uncertain nonaffine nonlinear systems with input saturation using neural networks[J].IEEE Transactions on Neural Networks and Learning Systems,2015,26(10):2311-2322
[6] Chen C L P,Liu Y J,Wen G X.Fuzzy neural network-based adaptive control for a class of uncertain nonlinear stochastic systems[J].IEEE Transactions on Cybernetics,2014,44(5):583-593
[7] Liu Z,Wang F,Zhang Y,et al.Fuzzy adaptive quantized control for a class of stochastic nonlinear uncertain systems[J].IEEE Transactions on Cybernetics,2016,46(2):524-534
[8] Wen Y T,Ren X M.Neural networks-based adaptive control for nonlinear time-varying delays systems with unknown control direction[J].IEEE Transactions on Neural Networks,2011,22(10):1599-1612
[9] Wang F,Chen B,Lin C,et al.Adaptive neural network finite-time output feedback control of quantized nonlinear systems[J].IEEE Transactions on Cybernetics,2018,48(6):1839-1848
[10] 馬敏,王桐,邱劍彬.含有執(zhí)行器故障的非線性切換互聯(lián)大系統(tǒng)的自適應(yīng)模糊Backstepping容錯(cuò)控制[J].南京信息工程大學(xué)學(xué)報(bào)(自然科學(xué)版),2018,10(6):665-675
MA Min,WANG Tong,QIU Jianbin.Adaptive fuzzy backstepping fault-tolerant control for nonlinear large-scale interconnected switched systems with actuator failures[J].Journal of Nanjing University of Information Science & Technology (Natural Science Edition),2018,10(6):665-675
[11] Liu W H,Lim C C,Shi P,et al.Backstepping fuzzy adaptive control for a class of quantized nonlinear systems[J].IEEE Transactions on Fuzzy Systems,2017,25(5):1090-1101
[12] Li D P,Liu Y J,Tong S C,et al.Neural networks-based adaptive control for nonlinear state constrained systems with input delay[J].IEEE Transactions on Cybernetics,2019,49(4):1249-1258
[13] Xu B,Shi Z K,Yang C G,et al.Composite neural dynamic surface control of a class of uncertain nonlinear systems in strict-feedback form[J].IEEE Transactions on Cybernetics,2014,44(12):2626-2634
[14] Tong S C,Li Y M.Adaptive fuzzy output feedback tracking backstepping control of strict-feedback nonlinear systems with unknown dead zones[J].IEEE Transactions on Fuzzy Systems,2012,20(1):168-180
[15] Wang M,Liu X P,Shi P.Adaptive neural control of pure-feedback nonlinear time-delay systems via dynamic surface technique[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B (Cybernetics),2011,41(6):1681-1692
[16] Zhang T P,Wen H,Zhu Q.Adaptive fuzzy control of nonlinear systems in pure feedback form based on input-to-state stability[J].IEEE Transactions on Fuzzy Systems,2010,18(1):80-93
[17] Chen B,Lin C,Liu X P,et al.Adaptive fuzzy tracking control for a class of MIMO nonlinear systems in nonstrict-feedback form[J].IEEE Transactions on Cybernetics,2015,45(12):2744-2755
[18] Yu Z X,Li S G,Li F F.Observer-based adaptive neural dynamic surface control for a class of non-strict-feedback stochastic nonlinear systems[J].International Journal of Systems Science,2016,47(1):194-208
[19] Liu T F,Jiang Z P,Hill D J.A sector bound approach to feedback control of nonlinear systems with state quantization[J].Automatica,2012,48(1):145-152
[20] Li F B,Shi P,Wu L G,et al.Quantized control design for cognitive radio networks modeled as nonlinear semi-Markovian jump systems[J].IEEE Transactions on Industrial Electronics,2015,62(4):2330-2340
[21] Liu W H,Lim C C,Shi P,et al.Observer-based tracking control for MIMO pure-feedback nonlinear systems with time-delay and input quantisation[J].International Journal of Control,2017,90(11):2433-2448
[22] Elia N,Mitter S K.Stabilization of linear systems with limited information[J].IEEE Transactions on Automatic Control,2001,46(9):1384-1400
[23] Wang M,Liu X,Shi P.Adaptive neural control of pure-feedback nonlinear time-delay systems via dynamic surface technique[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B (Cybernetics),2011,41(6):1681-1692
[24] Xu B,Shi Z,Yang C,Sun F.Composite neural dynamic surface control of a class of uncertain nonlinear systems in strict-feedback form[J].IEEE Transactions on Cybernetics,2014,44(12):2626-2634
[25] Shen Q K,Jiang B,Cocquempot V.Adaptive fuzzy observer-based active fault-tolerant dynamic surface control for a class of nonlinear systems with actuator faults[J].IEEE Transactions on Fuzzy Systems,2014,22(2):338-349
[26] Li Y M,Tong S C,Liu L,et al.Adaptive output-feedback control design with prescribed performance for switched nonlinear systems[J].Automatica,2017,80:225-231
[27] Yu Z,Li S,Li F.Observer-based adaptive neural dynamic surface control for a class of non-strict-feedback stochastic nonlinear systems[J].International Journal of Systems Science,2016,47(1):194-208
[28] Yoo S J.Adaptive tracking control for uncertain switched nonlinear systems in nonstrict-feedback form[J].Journal of the Franklin Institute,2016,353(6):1409-1422
[29] Yu J,Zhao L,Yu H,et al.Fuzzy finite-time command filtered control of nonlinear systems with input saturation[J].IEEE Transactions on Cybernetics,2018,48(8):2378-2387
[30] Wang F,Chen B,Liu X P,et al.Finite-time adaptive fuzzy tracking control design for nonlinear systems[J].IEEE Transactions on Fuzzy Systems,2018,26(3):1207-1216
[31] Liu L,Liu Y J,Tong S C.Neural networks-based adaptive finite-time fault-tolerant control for a class of strict-feedback switched nonlinear systems[J].IEEE Transactions on Cybernetics,2019,49(7):2536-2545
[32] Li Y M,Li K W,Tong S C.Finite-time adaptive fuzzy output feedback dynamic surface control for MIMO nonstrict feedback systems[J].IEEE Transactions on Fuzzy Systems,2019,27(1):96-110
Adaptive finite-time dynamic surface control for nonlinear systems with
input quantization and unknown disturbances
QI Xiaojing1 LIU Wenhui1
1 School of Electrical and Automation Engineering,Nanjing Normal University,Nanjing 210023
Abstract In this paper,the problem of finite-time adaptive output feedback dynamic surface control is studied for a class of nonlinear systems with quantized input signals and unknown disturbances.In the control design process,the nonlinear terms in the system are approximated by the fuzzy logic systems.A hysteretic quantizer is introduced to avoid chattering in the quantized signals,and the fuzzy state observer is constructed to estimate the unmeasurable states of the system.In order to propose the finite-time control strategy,firstly,a semi-global practical finite-time stability criterion is given.On this basis,an adaptive fuzzy controller is designed by combining the dynamic surface control technology with backstepping method.The controller can not only ensure that the observer and tracking error converge to a small neighborhood of the origin in a finite time,but also keep all the signals in the closed-loop system bounded.Finally,a simulation example is given to verify the effectiveness and feasibility of the control method.
Key words quantized input signals;fuzzy logic systems;dynamic surface control;backstepping;finite-time tracking control
收稿日期 2019-12-24
資助項(xiàng)目
國家自然科學(xué)基金青年基金(61803208);江蘇省自然科學(xué)基金青年基金(BK20180726);江蘇省高校自然科學(xué)研究面上項(xiàng)目(18KJB120005)
作者簡(jiǎn)介
劉文慧(通信作者),女,博士,講師,研究方向?yàn)榉蔷€性控制、智能控制.liuwenhui1211@163.com