Qing ZHENG ,Zhiqiang GAO

1.Department of Electrical and Computer Engineering,Gannon University,Erie,PA 16541,U.S.A.;

2.Center for Advanced Control Technologies,Department of Electrical and Computer Engineering,Cleveland State University,Cleveland,OH 44115,U.S.A.

1 Introduction

As the object to be controlled,physical plants in real world are not just nonlinear and time-varying but also highly uncertain.As the well-known control theorist Roger Brockett puts it:“If there is no uncertainty in the system,the control,or the environment,feedback control is largely unnecessary”[1].For much of its history,however,mathematical control theory has been developed largely based on the premise that a physical plant behaves rather closely as its mathematical model describes.Serving as the point of departure in control system design,this assumption does not reflect either the necessity of feedback control,nor the physical reality.The premise of model has stimulated in the last few decades lively debates and rapid new developments,such as those under the umbrella of robust,adaptive,and nonlinearcontrol.Butthe dependence on the model proves difficultto shake loose even though the engineering practice of automatic control has taught us that PID,with over a hundred years of history,is still the king,and that engineers by and large have little use of design techniques premised on a detailed mathematical model of the physical process to be controlled.

The practicality of a model-based design can be problematic in two regards:1)it could be rather expensive to obtain a detailed mathematical model;2)even if such a model is obtained,the uncertainties in the process,particularly the changes in the system dynamics,could easily render such model obsolete during operation.Such problems prove to be hard to overcome even with the most advanced techniques such as those known as robustcontrol,where the controlleris made tolerantofuncertainties to some degree but still requires nonetheless a fairly detailed and accurate mathematical model of the plant.For example,the robust control design methodology based on the small gain theorem does allow a small amount of uncertainties in plant dynamics,but not anywhere near the magnitude often encountered in practice.The problem of controlling a process of a large amount of dynamic uncertainties remains unsolved until a new paradigm,namely active disturbance rejection control(ADRC),came to the scence.

The ADRC resonates with practical minded researchers from the very beginning when Han argued that there must be a way to control a process independent of its mathematical model.The framework and conceptual underpining gradually took shape in the span of two decades between 1989 and 2009,as explained in[2–11].They fundamentally differ from other disturbance-centric design methods in the very concept of disturbance,which has been widely taken as forces external to the process to be controlled.Han inherited the notion of disturbance from H.S.Tsien that is more general and inclusive,including both the internal as well as the external disturbance.Tsien coined the term internal disturbance but Han took it to the next level,namely the total disturbance,which could very well be a function of the states of the process.In doing so,Han found a way to deal with the problem of large amount of dynamic uncertainties and to escape from the suffercating hold of the model-based design methodology.

The solution turns out to be a simple one:treat the process dynamics and external disturbance alike;lump them into a whole called the total disturbance;and then find a way to estimate and cancelit,reducing the process dynamics to an ideal,disturbance-free form.It is therefore obvious that whole enterprise comes down to the question of if such disturbance can be indeed estimated and cancelled.To this end Han left us with the extended state observer(ESO)[4],in a manner of experimental science:daring hypothesis followed by two decades of meticulously constructed tests,both in simulation and experimentations.In fact,Han pioneered the method of investigation in search of effective control mechanisms using computer simulation as the main tool[11].

To be sure,disturbance estimation and cancellation has been studied by many researchers over the years and many solutions have been offered,such as the unknown input observer(UIO)[12–19],the disturbance observer(DOB)[20–27],and the perturbation observer(POB)[28–31].Two recent surveys can be found in[32,33].The key difference from ADRC is that they are intended to compliment the existing model-based paradigm rather than replacing with a new one.The new ADRC paradigm started slowly but picked up speed recently,largely propelled by its large scale adoption as a viable industrial solution,threatening the dominance of the PID solution.What was for a long time an experimental solution all the sudden acquired the attention of researchers intending to grasp its stability properties.This paper summarizes some recent results in the analysis of linear ADRC(LADRC)and offers explanations in the frequency response language with which practicing engineers are familiar.

Ais Hurwitz for the αi,i=1,2,...,n+1,chosen above.

The paper is organized as follows.The time domain formulation of the ADRC is presented in Section 2.The engineering insight from frequency responses is discussed in Section 3.The time domain and frequency domain connection is given in Section 4.The time domain validation is shown in Section 5.The paper ends with a few concluding remarks in Section 6.

2 Time domain formulation of the ADRC

The ADRC was originally proposed as a combinatin of a tracking differentiator(TD)plus an ESO with a nonlinearform[3].The key ofthe ADRC is the ESO.In[41],the ADRC was proposed to be realized with a PD controller and a linear extended state oberser(LESO),formulating a LADRC.In this paper,the presented ADRC approach refers to LADRC.

伴隨就業(yè)問(wèn)題的凸顯，大學(xué)生在就業(yè)中的焦慮問(wèn)題越越來(lái)越普遍。就業(yè)焦慮按照在就業(yè)問(wèn)題上的具體表現(xiàn)分為：沖動(dòng)型（高焦慮階段高效能）、無(wú)助型（低焦慮階段、效能低）、穩(wěn)健型（低焦慮階段 高效能）、冷漠型（低焦慮階段、低效能）

First the ESO design is presented.Consider a generally nonlinear time-varying dynamic system with singleinput,u,and single-outputy,

wherewis the external disturbance andbis a given constant.Heref(y(n-1)(t),y(n-2)(t),...,y(t),w(t)),or simply denoted asf,represents the nonlinear time-varying dynamics of the plant that is unknown.That is,for this plant,only the order and the parameterbare given.The ADRC is a unique method designed to tackle this problem.It is centered around estimation of,and compensation for,f.To this end,assumingfis differentiable and leth= ˙

f,(1)can be written in an augmented state space form

wherex=[x1x2···xn+1]T∈Rn+1,u∈R andy∈R are the state,input and output of the system,respectively.Any state observer of(2),will estimate the derivatives ofyandfsince the latter is now a state in the extended state model.Such observers are known as ESO.

本文試圖從Github開源社區(qū)軟件開發(fā)演進(jìn)過(guò)程的數(shù)據(jù)入手，通過(guò)存儲(chǔ)庫(kù)數(shù)據(jù)挖掘的方法找到一種能夠?qū)﹂_源軟件的成功度進(jìn)行客觀量化度量的、簡(jiǎn)單易行的新方法，從而使能開源軟件開發(fā)團(tuán)隊(duì)快速了解所開發(fā)軟件的成功程度和團(tuán)隊(duì)狀況，明確影響開源軟件成功的關(guān)鍵因素，指導(dǎo)開源軟件的領(lǐng)導(dǎo)者采取合適的行動(dòng).

綜上所述，安列克聯(lián)合縮宮素和益母草比單獨(dú)縮宮素聯(lián)合益母草預(yù)防前置胎盤產(chǎn)后出血的療效好并且安全，值得臨床推廣。

Withuandyas inputs,the ESO of(2)is given as

⑥后期維護(hù)便利性。治理工程后期維護(hù)要方便簡(jiǎn)捷，工程不需要頻繁維護(hù)，工程在遭到簡(jiǎn)單破壞后能完成自我修復(fù)。

wherex=[x1x2···xn+1]T∈Rn+1,andli,i=1,2,...,n+1,are the observer gain parameters to be chosen.In particular,let us consider a special case where the gains are chosen as

The first attempt at the rigorous study of stability of the ADRC solution can be found in[34],where,for the sake of ease,the nonlinear gain structure of the original ADRC is replaced with a linear one.For the first time the convergence of the ESO and the bound on the tracking error in the ADRC were established,which lent support to the engineering success ofthe ADRCand furtherstimulated research interests on the subject.The research has grown more intensely and fruitfully since then,as can be seen in the more recent publications in[35–40].But there is one nagging problem that refuses to go away:the more rigorous study of ADRC has done little to provide guidance to its engineering applications.This paper intends to address this issue,as one of the languages.In particular,we believe that the language of the time domain analysis based on solving differential equations must be intimately connected with the language of frequency responses with which engineers are familiar.This can be done,as shown earlier in[34],by solving the differential equation and examining the properties of the solutions directly,instead of using the Lyapunov type of methods that tend to be rather conservative and cubersome.

and ωo,the observer bandwidth,becomes the only tuning parameter of the observer.

When a system model is known,then with the given functionh,the ESO of(2)now takes the form of

蘭江大橋高高地矗立著。蔣大偉把車開到路邊，兩人仰頭看著大橋。橋上，車輛在不停地穿梭流動(dòng)。蔣大偉低聲地：這就是蘭江大橋！鄭馨默默看著大橋，沒有說(shuō)話。蔣大偉繼續(xù)說(shuō)道：我沒說(shuō)錯(cuò)吧，蘭江大橋很有氣勢(shì)，很美。鄭馨還是沒說(shuō)話，她打開車門，蔣大偉：就這么下車了，不想再說(shuō)點(diǎn)什么嗎？鄭馨仍然沒說(shuō)話，蔣大偉急了：等等！臨死之前，還有什么未了之事，或許我能再幫你個(gè)忙。鄭馨皺緊眉頭，像竭力想著什么。蔣大偉說(shuō)：反正是快死的人了，沒什么不好說(shuō)的。鄭馨想了想，咬牙切齒地：我想等陳曉來(lái)，讓他看看我是怎么死的！

Assume that the control design objective is to make the outputofthe plantin(1)follow a given,bounded,reference signalr,whose derivatives,˙r,¨r,...,r(n),are also bounded.Let[r1r2...rnrn+1]T=[r˙r1···˙rn-1˙rn]T.Employing the ESO of(2)in the form of(3)or(6),the ADRC control law is given as

whereki,i=1,2,...,n,are the controller gain parameters selected to makesn+knsn-1+...+k1Hurwitz.The closed-loop system becomes

Note that with a well-designed ESO,the first term in the right hand side(RHS)of(8)is negligible and the rest of the terms in the RHS of(8)constitutes a generalized PD controller with a feedforward term.It generally works very well in applications but the issues to be addressed are:1)the stability of the closed-loop system(8);and 2)the bound of the tracking error.Note that the separation principal does not apply here because of the first term in the RHS of(8).

3 Engineering insight from frequency responses

Most of the development and analysis of the ADRC have only been shown in time domain.In[42],frequency-domain analysis of the ADRC is performed to quantify its performance and stability characteristics.In[43],it is shown that the amount of uncertainties can be reduced by way of active disturbance rejection,implemented in an inner loop to produce a well-behaved plant,which is then regulated by another controller in the outer loop.In[44],the ESO is brought into the frequency domain to show to what degree it forces the plant to behave like cascaded integrators and what can be done to improve the performance when the ESO is bandwidth limited.Some rigorous analysis for the frequency domain properties of ADRC has been given in[45].

3.1 Frequency response analysis

Consider a linear time-invariant second-order plant:

witha0anda1unknown,f=-a1˙y-a0yin this particular case.Since both the plant and the controller are linear,the robustness of the control system can be evaluated using frequency response.If ADRC indeed estimatesfand cancels it out,then we should see very little change in bandwidth and stability margins whena0anda1vary.

The Bode plots of the loop gain transfer function are shown in Fig.1.With ωc= ωo=100 rad/s,b=206.25,a1=3.085,anda0=[0 0.1 1 10 100],Fig.1(left)shows that,remarkably,gain margin,phase margin and cross-over frequency are almost immune to changes ina0.Similarly,with ωc= ωo=100 rad/s,b=206.25,a0=0,anda1=[0.1 1 3.085 10 100],Fig.1(right)demonstrates that gain margin,phase margin and crossover frequency are just as insensitive to changes ina1as to those ina0.

從對(duì)落實(shí)基礎(chǔ)知識(shí)調(diào)查的問(wèn)卷調(diào)查數(shù)據(jù)發(fā)現(xiàn)，有24.78%的學(xué)生需要老師對(duì)基礎(chǔ)知識(shí)講的更詳細(xì)，這部分學(xué)生基礎(chǔ)知識(shí)落實(shí)較差，需要另外采取措施去落實(shí)和鞏固，不然沒有扎實(shí)的基礎(chǔ)知識(shí)的學(xué)生談何提升能力，談何培養(yǎng)學(xué)生的地理核心素養(yǎng)。針對(duì)學(xué)生反映的課前預(yù)習(xí)效果偏低的問(wèn)題，可從教師和學(xué)生兩方面共同解決。教師采取課堂檢查和督促課后鞏固相結(jié)合。

Fig.1 Loop gain Bode plots at different a0 and a1.(a)and(c):a0=0,0.1,1,10,100.(b)and(d):a1=0.1,1,3.085,10,100.

First,consider the ESO with the given model of the plant.Let?xi=xi-xi,i=1,2,...,n+1.From(2)and(6),the observer estimation error for the system with a given model can be shown as

3.2 Uncertainty reduction through active disturbance rejection

The theme ofmodern controlis how to getaround the unknowns,i.e.,model uncertainties and disturbances,so that they do not degrade what is valued:stability and performance.In[43],it is demonstrated that the uncertainty stemming from both the external disturbance and the unknown internal dynamics,which is the subject of intense research efforts in the last few decades,can be greatly reduced through active disturbance rejection.Accordingly,it is demonstrated that control of uncertain system can be carried out in two steps:1)reducing the uncertain plant,via active disturbance rejection,to a class of cascaded integral plants;and 2)design the front end controller for these compensated plants.

得到老師的表?yè)P(yáng)后，越來(lái)越多的孩子開始幫助小鵬。一天，我看到教室地上有個(gè)塑料瓶和一張廢紙片，就在我準(zhǔn)備彎腰的一剎那，一個(gè)學(xué)生搶先一步撿走了塑料瓶，然后對(duì)著小鵬喊：“小鵬，給你一個(gè)塑料瓶?！倍厣系募埰瑓s一直無(wú)人問(wèn)津。頓時(shí)，我眼前一亮：如果紙片也能像塑料瓶一樣有人收集利用、變廢為寶的話，那些躺在地上的廢紙就不會(huì)無(wú)人理睬了，是不是也會(huì)像塑料瓶那樣被孩子們搶著撿呢？

某高速公路設(shè)計(jì)速度為100km/h，路基寬度28m，路面結(jié)構(gòu)形式為半剛性基層瀝青路面，基層設(shè)計(jì)為34cm水泥穩(wěn)定碎石，考慮到開放交通后，交通量較大，可能有大量重型運(yùn)輸車輛通行，研究決定在試驗(yàn)路使用異步連續(xù)攤鋪技術(shù)進(jìn)行半剛性基層施工，以提高基層整體性與承載能力，為該技術(shù)在全線展開打下基礎(chǔ)。

To demonstrate the effectiveness of the ESO in uncertainty reduction,consider a second order plant with wherer0is the modeling error in steady state,r∞is an uncertainty scalar at high frequency,and τ-1is the frequency at which the system is completely unknown.Herer0=1,τ-1=0.2π,andr∞=5.The perturbed plant is of the form:

If the ESO can fairly estimate the total disturbance,then the purtubed plant(10)can be reduced to¨y=u0.Bode plots of the transfer function for the plant¨y=u0fromu0toyare shown for different observer bandwidths in Fig.2.It demonstrates the amount of uncertainty reduction by the ESO.Clearly,the quality of uncertainty reduction is directly correlated to the bandwidth:the higher the ωo,the closer the compensated plant is to the ideal double integral plant.From Fig.2 it is concluded that the plant fromu0toyis reduced to a pure double integrator with very small error up to the frequency of 0.1ωo.That is,the control design problem is reduced to dealing with a pure double integral plant at or below the frequency of 0.1ωo.

走進(jìn)柳州，郁郁蔥蔥的城市綠化和優(yōu)美宜人的生態(tài)環(huán)境，映入眼簾。一如廣西許多地方一樣，山清水秀，鳥語(yǔ)花香。然而在保持這樣的生態(tài)環(huán)境之下，柳州被冠以“西南工業(yè)重鎮(zhèn)”的名號(hào)。工業(yè)化與生態(tài)環(huán)境建設(shè)，不可多見地在柳州和睦相處。

Fig.2 Magnitude plot of the compensated plant.

3.3 The enhanced ADRC design with a low observer bandwidth

Fig.3 Single integral plant acting as double integral.

By assigning allpoles ofthe observertoωo,denoted as the observer bandwidth,the process of selecting gains in ESO becomes one of simply tuning ωo.The modified plant,i.e.,the transfer function fromu0toy,can be shown as

Note that the denominator contains a low-pass filter of ordern+1 with a corner frequency of ωo.If this is imagined as an ideal filter,where it acts as unity gain at and below the corner frequency but zero gain above it,the frequency response of(11)can be expressed as

It can be seen from(12)that the modified plant acts as perfect integrators of ordernwithin the bandwidth of the observer.At high frequencies,it will instead follow the response of the plant.It can be assumed that if an infinite bandwidth could be selected in an ideal world without noise or sampling,then the plant would indeed act as a perfect integral of ordernregardless ofGp.By rearranging(11)as(13),the transition from the desired integral form at low frequency to the original plant at the high frequency can be captured by the transfer function ofˉGp(s)in the form of(13),where a low-pass filter shapes the plant into the integral form at low frequency and a high-pass filter shapes the plant at high frequency.

從表2可以看出，實(shí)驗(yàn)組學(xué)生在出科考核中的理論知識(shí)、操作技能、臨床思維方面均優(yōu)于對(duì)照組學(xué)生，由此得出結(jié)論，采用虛擬技術(shù)輔助教學(xué)模式的實(shí)驗(yàn)組教學(xué)效果明顯優(yōu)于采用傳統(tǒng)教學(xué)方法的對(duì)照組。從表3 可以看出，在教學(xué)滿意評(píng)價(jià)調(diào)查問(wèn)卷中，實(shí)驗(yàn)組的各項(xiàng)滿意度評(píng)價(jià)均高于對(duì)照組。

Fig.4 Closed-loop poles for the 1st order plant as ωo varies.

This pattern is the same for any stable first-order plant.By monitoring how the poles ofthe modified plant move,it can be better understood how ADRC forces the plant to behave like cascaded integrators.The information about the imperfection can be used in the control design to better accommodate the remaining dynamics beyond the cascaded integrators.

4 Time domain and frequency domain connection

In[41],the ESOand the associated controllerwere parameterized,thusly LESO and LADRC were formulated.In that paper,the observer bandwidth and controller bandwidth,which engineers are familiar with,were first connected to ESO and ADRC as the tuning paramters.The parameterization of ESO and ADRC makes the concept very easy to understand and implement by engineers,therefore widely used in practice[46–48].Critical to the connection between time domain formulation and frequency domain insights is established through the use of bandwidth in time domain analysis.

In many real world scenarios,the plant dynamics represented byfis mostly unknown.The ESO design for a system with dynamics largely unknown is shown below.

With the parameterized ADRC,the first attempt at the rigorous study of stability of the ADRC solution can be found in[34].For the first time the convergence of the ESO and the bound on the tracking error in the ADRC were established by solving differential equations.The detailed derivations are given in[49].

4.1 Convergence of the ESO error dynamics

The results show that the active disturbance rejection based control system possesses a level of robustness that is rarely seen.The bandwidth and stability margins,in particular,are kept almost unchanged as the plant parameters vary significantly.

加強(qiáng)學(xué)習(xí)提升素質(zhì)。每月組織1-2次學(xué)習(xí)會(huì)，集中學(xué)習(xí)《中國(guó)共產(chǎn)黨紀(jì)律處分條例》、《中國(guó)共產(chǎn)黨問(wèn)責(zé)條例》等黨規(guī)黨紀(jì)、中央最新精神以及業(yè)務(wù)工作知識(shí)，加強(qiáng)平時(shí)自學(xué)，注重理論聯(lián)系實(shí)際，不斷提升隊(duì)伍素質(zhì)、能力，夯實(shí)監(jiān)督執(zhí)紀(jì)工作基礎(chǔ)。

此方法需要把各方面變動(dòng)所形成的差距聯(lián)系到一起，然后再逐一進(jìn)行分析，整個(gè)過(guò)程的關(guān)鍵點(diǎn)在于需要確定“虧損邊界點(diǎn)”，然后在對(duì)其展開工作，需要注意的是，動(dòng)態(tài)上所出現(xiàn)的變動(dòng)因素與單位的盈虧是存在一定關(guān)聯(lián)的，而這對(duì)單位從經(jīng)營(yíng)決策中采取相應(yīng)措施有很大的幫助。

The proof of this theorem has been given in[50].

When the plantdynamics is largely unknown,the ESO is designed as shown in(3).Consequently,the observer estimation error becomes

The proof of this theorem has been given in[49].

她爸爸是誰(shuí)？大家不約而同地在心中畫了個(gè)問(wèn)號(hào)。馬縣長(zhǎng)走到何副書記面前說(shuō)：“老何，殊書是吳軍同志的女兒，望你們?cè)谏钌隙嗾疹欬c(diǎn)兒?！?/p>

In summary,when the plant model is given and used in the ESO,the dynamic system describing the ESO estimation error is asymptotically stable;and in the absence of such model,the ESO estimation error is bounded and its upper bound monotonously decreases with the observer bandwidth.The stability characteristics of the ADRC,where the ESO is employed,is presented next.

4.2 Stability characteristics of the ADR C

In[49],the stability characteristics of the ADRC are presented for both the cases of the plant model given and plant dynamics largely unknow.

For the case of the plant model given,one has the following theorem.

Theorem 3Assumingh(x,u,w,˙w)is globally Lipschitz with respect tox,there exist constants ωo＞ 0 and ωc＞ 0,such that the closed-loop system(8)is asymptotically stable.

Now we consider the case that the plant dynamics is unknown and the ESO in the form of(3)is used instead.

The proofofTheorems 3 and 4 can be found in[49].In summary,with the given model of the plant,the closedloop system(8)is asymptotically stable;and with plant dynamics largely unknown,the tracking error and its up to(n-1)th order derivatives of the ADRC are bounded and their upper bounds monotonously decrease with the observer and controller bandwidths.

The above analyses show thatthe observerbandwidth and control loop bandwidth are associated in the time domain stability analysis with the upper bounds of the observer error and the tracking error,respectively.This makes such analysis relevant to the common design considerations and concerns shared by practicing engineers.

5 Time domain validation

With the convergence ofthe ESOand the ADRC established,a simulation study of nonlinear plant with partial model information is presented below.

Consider the following nonlinear system

wherefrepresents the summation of the plant dynamics˙y3+yand the external disturbanced.

Note that for a second order plant,the LESO in(6)and(3)is of the third order,wherex3is an estimate off.With a well-tuned observer,the control law is given

wherek1= ω2c,andk2=2ωc.

The LADRC tracking performance is shown in Fig.5 under three different scenarios:1)fis completely unknown;2)only partial internal dynamics information of the plantis given,i.e.,fpartial=˙y3;3)the internaldynamics of the plantfinis completely known,i.e.,fin=˙y3+yis given.In this simulation,the tuning parameters are ωc=4.5 rad/s and ωo=20 rad/s.Fig.5 shows the tracking errors between the reference and the output for three cases using a step input att=1 s as the excitation and a pulse disturbance with the amplitude of±20,the period of 4 s,the pulse width 5%of the period,and the phase delay of 4 s.From Fig.5,it can be observed that the tracking error of the control loop decreases as more model information is incorporated into the LADRC.

Fig.5 The LADRC performance with different LESOs for the nonlinear system(LESO1:without plant information;LESO2:with partial plant information,i.e.,f partial=˙y3;LESO3:with complete plant information,i.e.,f in=˙y3+y is given).

Note that the system(18)is a nonlinear system.The above simulation demonstrates that ADRC can control the nonlinear system with large uncertainties very well although the ADRC itself is linear.Many other approaches can deal with uncertainties,however,most of them can only handle small uncertainties.ADRC is a very simple and straightforward approach.It is easy to understand by engineers and easy to implement in real applications.From the above simulation,even˙f,i.e.,htends to∞,the ESO gain is still low.Therefore,the ESO is not a high gain observer.

6 Conclusions

In this paper,the time domain formulation and frequency domain understanding of the ADRC are connected.It is shown that the formulation of the ADRC in time domain can be easily understood by engineers with insights in the language of frequency responses,such as the bandwidth and stability margins.From both the frequency responses and the time domain validation,it is clear that the ADRC is unique in its ability of disturbance rejection and in its robustness to large uncertainties in process dynamics.It also shows that the stability characteistics of the ESO and the ADRC can be analyzed directly by solving the differential equations,instead of indirectly by using the standard techniques such as the Lyapunov methods.In doing so,the relationship between the error bounds and the ADRC bandwidth is disclosed.In the ADRC analysis and validation,one can see that the ADRC can handle nonlinear systems with large uncertainties and disturbances without the need of accurate mathematical model of the plant.Partial model information,if given,can and should be incoporated into the ESO for better performance,less noise sensitivity and the reduced bandwidth.

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