2000 ieee tcst canudas praly

8/8/2019 2000 IEEE TCST Canudas Praly

1/10

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 8, NO. 5, SEPTEMBER 2000 757

Adaptive Eccentricity CompensationCarlos Canudas de Wit and Laurent Praly

AbstractThis paper is devoted to the problem of rejecting

oscillatory position-dependent disturbances with unknown fre-quency and unknown amplitude. The considered disturbancesare here assumed to be produced by eccentricity in mechanicalsystems and drives. Most of the previous works on eccentricitycancellation assume a time-depending oscillation, we insteadassume that the oscillatory disturbance is position-dependent.This leads us to formulate and to globally solve the adaptivecancellation problem using a velocity-dependent internal modelof the eccentricity. The proposed control design results in anasymptotically globally stable adaptive eccentricity compensator(AEC). An apparatus with rolling eccentricity has been build totest the controller. The paper presents a serial of experimentalresults showing the improvements of this controller. Also a com-parative study with a simple porortional integral (PI) regulatoris presented.

Index TermsAdaptive compensation, eccentricity sinusoidaldisturbance rejection.

I. INTRODUCTION

WE consider systems of the form(1)

where is the system angular position, is the inertia, isthe control input and is the position-dependent oscillatorydisturbance defined as

(2)

It is assumed that the amplitude , the dimensionless frequency, and the phase of the disturbance are unknown. The

problem considered here is thus to cancel the effect of the dis-turbance in the system (1).

This type of problem arises as a consequence of eccentricityin many mechanical systems where the center of rotation doesnot corresponds with its geometric center. This is typically thecase on drives with magnetic bearings. It also arises in systemswith friction where the contact forces change as a function ofthe position .

The dependency on position of can be visualized inthe following scenarios. It is known that the friction forces de-pends on the normal force acting between two surfaces. In-

Manuscript received October 28, 1998; revised May 2, 1999. Recommendedby Associate Editor, M. Bodson.

C. Canudas de Wit is with the Laboratoire dAutomatique de Grenoble,ENSIEG-INPG, 38402 St. Martin dHres, France (e-mail: [email protected]).

L. Praly is with Ecole des mines de Paris, Centre Automatique, 77305Fontainebleau, France (e-mail: [email protected]).

Publisher Item Identifier S 1063-6536(00)05747-X.

accuracies in the geometric position of the rotating axis of a

rolling mill (eccentricity), will produce position dependent dis-turbances. In gear boxes, friction will vary as a function of theeffective surface in contact with the gears teeth. The two-di-mensional rolling and spinning friction causes in ball bearingsthe frictional torque to be dependent on both position and ve-locity. Fig. 1 shows some of these examples.

Many of the existing works consider not as a position func-tion, but as a time-dependent exogenous signal, of the form

(3)

In theprevious mentionedsystem this hypothesis is only valid

if we assume that the system is operating and regulated, at con-stantvelocity sothat becomes proportional . Distur-bances of the form (3) have been considered in problems suchas active noise and vibration control. The noise is thusassumed to be generated by the rotating machinery and trans-mitted through thesensor path. Examples rate from engine noisein turboprop aircraft [5] to ventilation noise in HVAC system[6], passing through engine noise in automobiles [10].

The proposed solutions resort to standard adaptive algo-rithms if the frequency is assumed to be known [2]. Repetitivecontrol has also been used to compensate eccentricity in rolling[7]. For the general case where both amplitude and frequencyare unknown, some approaches based on the phase-lock loop

principle has been proposed [1], but without proof of stability.When formulating this problem in the time-domain, the maindifficulty to show global stability properties of the adaptive al-gorithms comes from the fact that the unknown parameters ap-pear nonlinearly in the (3).

When the system operates under time-varying velocity pro-files, in (3) becomes time-dependent generating a signalwith a large frequency contain. The position dependent distur-bance model (2) is thus better adapted for those cases.

In this paper, we present a globally stable adaptive algorithmthat solves the above mentioned problem. For this, we use a ve-locity-dependent state-space representation for . An adap-tive observer is thus designed ensuring global asymptotic sta-

bility. The observer is constructed such that can be used in openloop (for predicting oscillatory disturbances in view of diag-nostic applications), or in closed loop (to compensate for theeccentricity effect). The properties of the algorithm are invariantwith respect to the operating velocity .

The second part of the paper describes the experimental eval-uation of AEC-controller tested on a special purpose apparatusthat exhibits rolling eccentricity. The performance of the pro-posed controller is evaluated with several velocity signal pro-files (i.e., constant, time-varying), and finally we present a com-parison with the standard proportional integral (PI) controller.

10636536/00$10.00 2000 IEEE
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


2/10

758 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 8, NO. 5, SEPTEMBER 2000

Fig. 1. Examples of systems where disturbances d ( x ) are positions-dependent and may produce eccentricity: the upper left figure shows an example of a systemswhere the shear force acting on the surface of contact may vary as a function of the joint angle positions, due to eccentricity on the axis of rotation if r 6= r . Inthe system with magnetic bearing (upper right), the geometric axis of the cylinder does not coincide with the axis of rotation due to unbalanced masses distribution.The left lower figure shows the spinning and rolling resistances induce a variation on the effective area of contact as a function of the inner race position and in theright lower figure this variation is due to the relative position of the gears teeth.

II. CONTROL DESIGN

In this section we formulate the internal model for the dis-turbance , and then we present the control design that includesthe adaptive observer. We also present the stability properties ofsuch a design.

Internal model for : Let , be defined as

(4)

(5)

this gives the following state-space representation for

(6)

(7)

(8)

The above set of equation describes the velocity-dependentstate-space (internal) model for the eccentricity.

Observer structure: These equations together with systemequation (1) suggest the following observer structure:

(9)

(10)

(11)

where is an estimate for (the adaptation law for willbe designed latter). is an estimated for needed to design ourfull-order observer1 . , and are positive constants. The vari-

1It is also possible to design a reduced-order observer, but this results in anobserver that is highly noise sensitive. Instead, we have introduced in this paperthe full-order observer that has better noise rejection characteristics.

able is used to select between two possible operation modes:an eccentricity predictor if , or if the observer isto be used in closed loop as a eccentricity compensator. isthe velocity time-profile to be followed.

Error equations: We consider the problem of tracking thedesired velocity supposed to be bounded and continuous

as well as its derivative. To this aim we define the adaptive ec-centricity control (AEC) control as

(12)

with . is given by the observer above, and the adaptivelaw to be derived.

Introducing the following error definitions, we have that

the closed-loop error equations are

(13)

(14)

(15)

(16)

Adaptation law: The adaptation law is derived from the fol-lowing analysis. Introduce as

and this gives

(17)

(18)


3/10

CANUDAS DE WIT AND PRALY: ADAPTIVE ECCENTRICITY COMPENSATION 759

Fig. 2. Simulation results with v = 2 0 c o s ( = 2 ) t . Upper left: desired ( v ) and closed-loop system ( v ) velocity. Upper right: Velocity tracking error timeprofile. Lower left: Actual ( z ) and predicted ( z ) disturbance. Lower right: Time-evolution of ( t ) .

which suggests to define as

(19)

which cancels the term between the square brackets, resultingin in a seminegative function , i.e.,

Standard arguments along the Barlabats lemma can be here ap-plied to conclude that , and tend asymptotically to zero,while all the internal signal of the system remain bounded.

The representation of the adaptation law (19) is appropriatefor analysis. But it cannot be implemented directly in this wayunless the system acceleration, is assumed to be measurable(note that ). Alternatively, we can introduce achange of coordinates in and then show that measurement of

is not needed.To this aim, note that can be rewritten as

(20)

(21)

(22)

(23)

(24)

or equivalent

Defining the new variable as

(25)

we have


4/10
http://-/?-http://-/?-


5/10


Fig. 3. Schematic (top) lateral view of the experimental setup to studyeccentricity (bottom) front view.

Fig. 4. Control block scheme of the adaptive eccentricity compensator (AEC)with feedforward friction compensation.

TABLE IMOTOR, FRICTION, AND LOAD

PARAMETERS

They capture the distributed friction characteristics on themotor. is the friction coefficient of this integratedmodel2 .

2In the LuGre model is replaced with the function g ( v ) = F + ( F 0F ) e (where F is the Coulomb friction, F is the Sticktion frictionlevel, and v is the Stribeck velocity) in order to include the Stribeck effect. Forsimplicity reazons, we just use which is the Coulomb friction normalizedby the Normal force. Since our main objective here is to study the eccentricityeffects, we have explicitly introduced the normal forcedependency in the LuGremodel, and simplified the expression of g ( v ) .

Model (32) can be rewritten as

(35)

with

(36)

(37)

(38)

(39)

(40)

(41)

is the dimensionless frequency at the motor side (note that

the eccentricity frequency effect at the motor shaft is demulti-plied by the gear ratio). Note also that model (35) differs frommodel (1) by the presence of friction, and by the dependence of

on . Hence to apply to our previous adaptive eccentricitycompensator, we need to cancel the friction, and to assume that

, can be approximate by its steady-state value, as shown in (41).

Friction canbe canceled viafeedforward or feedback. The ap-proximation on may hold for most operation condi-tions (except forthe time periods when thevelocity crosses zero)since thefrictiondynamics of (34), is much faster than themotorvelocity dynamic (of the order of magnitude of . Theonly conceptual problem lies thus on the velocity zero crossingsthat occurs at isolated time periods. Its effects on theclosed-loopperformance will be evaluated through experiments.

Resuming, we have the following.

To apply our AEC strategy, we must first compensate forthe position-independent friction .

The dependency of and on isnot a problem since thedynamic of is faster than the one of , and .

The effects of sign-velocity dependency of on theclosed-loop system will not be explicitly considered, butthey will be evaluated through experiments.

We are thus in position to apply our control algorithm, andfor that we will use our AEC controller, with feedforward com-

pensation

(42)

where being a linear operator (when referring to the AECcontroller we will simply refer to a proportional velocity gain,i.e., ), is given by the set of (27)(30), and is afeedforward friction prediction obtained from

(43)

(44)


6/10


Fig. 5. Experiments under constant velocities v = 3 0 Rad/s. Upper left: Velocity tracking error ( v 0 v ) . Upper right: control input time profile. Lower left:predicted disturbance, z . Lower right: time-evolution of ( t ) .

Fig. 4 show the block diagram of the EAC control scheme withfeedforward friction compensation. Locally, , thus theclosed-loop equation with this additional friction compensationterm is similar to the frictionless system studied in the previoussection.

System parameters including friction coefficients have beenestimated using a similar procedure as described in [4]. Theyare reported in Table I.

The experiments were conducted on a dSPACE real-timecomputer system based on a digital signal processor, with asampling time of 1 ms. The position is measured by means ofa high-precision optical encoder of 120 000 divisions, yieldinga resolution of 0.52 rad. The velocity is is computed byposition differentiation over a sampling period. The majorsource of noise is thus caused by the numerical errors due tothis approximation.

B. Performance Evaluation under Different Tracking Signals

In this section, we presents several experiments aiming atevaluating the AEC control behavior under different operating

conditions: constant velocities, time-varying velocities, and ve-locity reversals. The control parameters used for these experi-ments were

Controller gains: (theand , gains are the PI-control gains used later for

comparisons. Observer gains: .

Adaptation gains: .1) Experiments Under Constant Velocities: The experi-ments in Fig. 5, show the tracking error, the prediction ,the estimate , and the control signal. The experiments arerealized at rad/s. The eccentricity compensation isapplied at s. It can be observed that the oscillatorydisturbance affecting the tracking error is canceled whenapplying the term , in . The remaining error, which isabout 12% of before compensation, is reduced to 1.5% withthe compensation. The time-profiles shown in all the presentedfigures have been low-pass filtered to extract noise.

The time evolution of , has a shape similar to a sinu-soidal wave (as it has been predicted). The imperfections may
http://-/?-http://-/?-


7/10


Fig. 6. Experiments under time-varying velocities v = 3 0 + 1 0 s i n ( = 2 t ) . Upper left: Velocity tracking error ( v 0 v ) . Upper right: control input time profile.Lower left: predicted disturbance, z . Lower right: Time-evolution of ( t ) .

be attributed to the nonuniform deformation of the wheel con-tact surface (the wheel in contact is composed of a inner unde-formable steel wheel, covered by a rubber 5 mm o-ring). The es-timated parameter is observed to converge (in average sense)to a value close to 0.055, which according to two implies that

. The theoretical value of is given by theexpression (39) as , which

seems to correspond to the experimental found value (note alsothat the period of gives for a velocity of 30 rad/s, an ex-perimental value of , for a ).

2) Experiments Under Time-Varying Velocities.: The ex-periments in Fig. 6, showthe tracking error, the prediction ,the estimated valueof , and thecontrol signal. The experimentsare realized under a positive time-varying desired velocity pro-file , under the same AEC controller.Note that the predicted does not necessarily resembles toa pure sinusoidal wave. Conceptually, the magnitude of thedisturbance , may change (not much) as a function of thevelocity (see discussion at the end of Section IV-A) due to theviscous friction term , in , but this seems to have a neg-

ligible effect on the closed-loop performance. The main (small)difference with respect to the previous experiment, is a slightlylarge level of noise. Nevertheless, the global system behavior ispreserved. Finally, note that the value of tends to a value sim-ilar to the one obtained for constant velocity.

C. Comparison Between the AEC and the PI Controllers

The effect of the disturbance on the output , in system(1), under a linear controller , can be quanti-fied by looking at the spectral distribution of sensitivityfunction

, where

(45)

In this section we compare the AEC-controller ,with a simpler PI-controller , usually used toregulate velocity. For this comparison to be fair, we set the gains

, and , such that the PI controller have a better rejectioncharacteristics than the P controller (linear part of the AEC), in


8/10


9/10


TABLE IIPERFORMANCE QUANTIFIERS OF THE COMPARISON BETWEEN THE AEC AND THE PI CONTROLLERS UNDER DIFFERENT VELOCITY PROFILES: v = 1 0 [rad/s],

v = 4 0 [rad/s], v = 2 0 + 1 0 s i n ( 0 : 7 8 t ) [rad/s], AND v = 4 0 + 1 0 s i n ( 0 : 7 8 t ) [rad/s]

the frequency domain where the spectral support of the is ex-pected to lie, i.e., ,and elsewhere. Fig. 7 shown the mag-nitudes of , and , with the given choice of parameters.

Fig. 8 shown the tracking error and the control signal for bothcontrollers with rad/s. At this velocity,

has its spectrum concentrated at 4.8 rad/s. FromFig. 7, we see that is about 8 dB smaller than the one ofthe AEC. Although, the performance of the AEC-controller im-provesoverthe PI, it maybe expected that this improvement willdiminish when operating at lower frequencies where issmall.

Table II shows several quantifiers (maximum value, meanvalues, variance, etc.) of the tracking error and the controlsignal under several different operation velocity profiles;

(see table description).

The first two set of trials correspond to constant velocities:one with low velocity and the other with higher velocity. Forthe low velocity case , the PI sensibility functionhas a substantial low magnitude. This is the less favorable casefor the AEC-controller. Nevertheless, both controllers perfor-mance are comparable. The explicit prediction and compensa-tion of the disturbance replace the need for a low magnitude ofthe sensitivity function at low frequencies. For larger velocities(i.e., ), the AEC-controller overperform the PI controller.

The second set of trials corresponds to time-varying velocityprofiles. In some applications it is required to operate undervelocity changes. Thus linear controllers tuned for a particularoperational velocity, may degrade its performance while con-

fronted to changes in . The experiment concerning the profileproduce a changeof velocitybetween tenand 30 rad/s, while

the profile produce a change of velocity between 30 and 50rad/s. From Table II, it can be observed that in all these trials,the AEC controller improves over the PI with equivalent controlauthority.

V. CONCLUSION

We have presented a method for compensating eccentricity inmechanical systems. As a main difference with previous works,

we have formulated the disturbance as a position-dependent pe-riodic function, leading to a velocity-dependent state space rep-resentation. This formulation seems to be justified in most ofthe mechanical applications where eccentricity occurs.

From this formulation, we have designed an adaptive pre-dictor which allows to reject the eccentricity effects studiedin this paper. The adaptive eccentricity compensator has beenshown to be asymptotically stable while ensuring internal signalboundedness.

We have also presented several experiments of the AEC thatdemonstrated the improvements over simpler linear controllers(PD and PID). The experiments were conducted under differentvelocity profiles with different amplitudes. The obtained resultshave demonstrated that the AEC mechanism improves over thelinear controllers. Besides that, and in opposition to some of theexisting mechanisms, our AEC do not require any gain retuningwhen operating at different velocities.

We have also observed that to the internal observer signalto behave well, it was important to compensate for the motorfriction. When this was not the case, the additional bias is inthe system due to inexact friction cancellation provokes a high-oscillatory behavior on the estimate (which can be due to oneof the well-known phenomena of drift, bursting, and parameterrecovery, see for example [9]). Although, this does not changemuch the time-profile of , this particular phenomena deservesmore attention.

ACKNOWLEDGMENT

The first author would like to thanks P. Posselius and H. Olof-sson for their help in performing the experiments and the inter-esting discussions that resulted during their stay at Grenoble.

REFERENCES

[1] M. Bodson and S. C. Douglas, Adaptive algorithm for the rejection ofperiodic disturbances with unknown frequency, Automatica, vol. 33,no. 12, pp. 22132221, 1997.

[2] M. Bodson, A. Sacks, and P. Khosla, Harmonic generation in adaptivefeedforward cancellation schemes, IEEE Trans. Automat. Contr., vol.39, pp. 19391944, 1994.

[3] C. Canudas de Wit, H. Olsson, K. J. strm, and P. Lischinsky, Anew model for control of systems with friction, IEEE Trans. Automat.Contr., vol. 40, pp. 419425, Mar. 1995.

[4] C. Canudas de Wit and P. Lischinsky, Adaptive friction compensa-tion with partially known dynamic friction model, Int. J. Adapt. Contr.Signal Process., vol. 11, pp. 6585, 1997.
http://-/?-http://-/?-


10/10


[5] U. Emborg and C. F. Ross, Active control in the Saab, in 340 Proc.2nd Conf. Recent Adv. Active Contr. Sound Vibrations, Blacksburg, VA,1993, pp. S67S73.

[6] L. J. Eriksson, A practical system for active attenuation in ducts,Sound Vibr., vol. 22, no. 2, pp. 3040, 1988.

[7] S. S. Garimella and K. Srinivasan, Application of repetitive control toeccentricity compensation in rolling, J Dyn. Syst., Meas., Contr., vol.118, pp. 657664, Dec. 1996.

[8] H. Olofsson and P. Posselius, Experimental Study of Adaptive

Eccentricity Compensation, Lab. Automat. Contr., Grenoble, France,Int. Rep., 1998.[9] M. Espaa and L. Praly, On the global dynamics of adaptive systems:

A study of an elementary example, SIAM J. Contr. Optimi., vol. 31, no.5, pp. 11431166, Sept. 1993.

[10] R. Shoureshi and P. Knurek, Automotive application of a hybrid activenoise and vibration control, IEEE Contr. Syst. Mag., vol. 16, no. 6, pp.7278, 1996.

Carlos Canudas de Wit was born in Villahermosa,Tabasco, Mexico, in 1958. He received the B.Sc.degree in electronics and communications from theTechnological Institute of Monterrey, Mexico, in1980. He received the M.Sc. and Ph.D. degrees inautomatic control from the Polytechnic of Grenoble,France, in 1984 and 1987, respectively.

Since 1987, he has been working at the same de-partment as Directeur de recherche at the CNRS,where he teaches and conducts research in the area ofadaptive and robot control. He is also responsible for

a team on control of electromechanical systems and robotics. He teaches under-graduate and graduated courses in robot control and stability of nonlinear sys-tems. He was a Visiting Researcher in 1985 at the Lund Institute of Technology,Sweden. His research topics include adaptive control, identification, robot con-trol, nonlinear observers, control of systems with friction, ac and CD drives,automotive control, and nonholonomic systems. He has written the book Adap-tive Controlof PartiallyKnown Systems: Theory and Applications(Amsterdam,The Netherlands: Elsevier, 1988). He also edited two books, Advanced RobotControl (New York: Springer-Verlag, 1991) and Theory of Robot Control, in theSpringer Control and Communication Series.

Dr. de Wit was Associate Editor of the IEEE TRANSACTIONS ON AUTOMATICCONTROL from January 1992 to December 1997 and has been Associate EditorofAutomatica since March 1999.

Laurent Praly received the engineering degreefrom cole Nationale Suprieure des Mines de Paris,France, in 1976.

After working in industry for three years, in1980 he joined the Centre Automatique et Systmesat cole des Mines de Paris. From July 1984 toJune 1985, he spent a sabbatical year as a VisitingAssistant Professor in the Department of Electricaland Computer Engineering, University of Illinois,

Urbana-Champaign. Since 1985, he has continuedat the Centre Automatique et Systmes, where heserved as Director for two years. In 1993, he spent a quarter at the Institute forMathematics and its Applications at the University of Minnesota, Minneapolis,where he was an Invited Researcher. His main interest is in feedback stabi-lization of controlled dynamical systems under various aspectslinear andnonlinear dynamic output, with parametric or dynamic uncertainty. On thistopic, he is contributing both on the theoretical aspect with many academicpublications and the practical aspect with several industrial applications.

2000 ieee tcst canudas praly

Documents