A Reduced-Order Nonlinear Clutch Pressure Observer for Automatic Transmission-T3Q

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446 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 18, NO. 2, MARCH 2010

A Reduced-Order Nonlinear Clutch Pressure Observerfor Automatic Transmission

Bingzhao Gao, Hong Chen, Member, IEEE, Haiyan Zhao, and Kazushi Sanada

AbstractFor a novel type of automatic transmissions adoptingclutch-to-clutch shift control technology with electro-hydraulic ac-tuators, a clutch pressure observer method based on input-to-statestability (ISS) is proposed. Model uncertainties including steadystate error and unmodelled dynamics are considered as additionaldisturbance inputs and the observer is designed in order thatthe error dynamics is input-to-state stable. Lookup tables, whichare widely used to represent complex nonlinear characteristicsof engine systems, appear in their original form in the designedreduced-order observer. The designed pressure observer is testedon an AMESim powertrain simulation model. Comparing withthe sliding mode method, the designed pressure observer has thebetter performance.

Index TermsAutomatic transmission (AT), clutch pressure es-

timation, input-to-state stability (ISS), nonlinearity, reduced-orderobserver.

I. INTRODUCTION

IN RECENT years, in order to improve fuel economy,reduce emission, and enhance driving performance, many

new technologies have been introduced in the transmissionarea, such as dual clutch transmission (DCT) and new auto-matic transmission (AT) controlling clutches independently[2]. Furthermore, smart proportional valves with large flow rate

are developed for direct clutch pressure control, without using

the pilot duty solenoid valve [3]. These valves can be used innew ATs to improve the ability of adapting to different driving

conditions, as well as to reduce cost and to improve packaging.In both DCTs and new ATs, the change of the speed ratio isregarded as the process of one clutch being engaged while theother being disengaged, namely, clutch-to-clutch shift. For the

vehicles with hydraulic cylinder as clutch actuator, which isubiquitous in the present transmissions, the cylinder pressure

Manuscript received March 27, 2008; revised March 03, 2009. Manuscriptreceived in final form May 31, 2009. First published August 11, 2009; currentversion published February 24, 2010. Recommended by Associate Editor F.Vasca. This work was supported in part by the National Science Fund of Chinafor Distinguished Young Scholars under Grant 60725311 and by the National

Nature Science Foundation China (90820302). This brief appeared in part at theIEEE-CDC, Cancun, Mexico, 2008.

B.-Z. Gao is with the National Automobile Dynamic Simulation Laboratory,JilinUniversity, Changchun 130025, PR China, and also with the Department ofMechanical Engineering, Yokohama National University, Yokohama 240-8501,Japan (e-mail: [email protected]).

H. Chen is with the Department of Control Science and Engineering, JilinUniversity (Campus NanLing), Changchun 130025, PR China, and also withthe National Automobile Dynamic Simulation Laboratory, 130025 Changchun,PR China, (e-mail: [email protected]; [email protected]).

H. Zhao is with the Department of Control Science and Engineering, JilinUniversity (Campus NanLing), Changchun 130025, PR China.

K. Sanada is with the Department of Mechanical Engineering, YokohamaNational University, Yokohama 240-8501, Japan (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TCST.2009.2024758

control becomes important for good shift quality. Sensorsmeasuring the clutch cylinder pressure, however, are seldomused because of the cost and durability. Hence, it is required toestimate the shaft torque or the cylinder pressure, in order toenhance control performance [4].

Because of the complex nonlinearities in automotive power-train, such as the speed-torque relationship of engines and thecharacteristics of torque converters, it is very hard to model the

whole dynamics with physical principles. Lookup tables, whichare obtained from large numbers of experiments in the steadystate, are widely used to describe the nonlinear characteristics.There inherently exist model uncertainties, such as steady-state

error and unmodelled dynamics. Moreover, the variation of thevehicle mass and the road grade also bring uncertainties to thepowertrain dynamics. Therefore, the clutch pressure/torque es-timator must be robust against the variation of powertrain pa-

rameters and the uncertainties.There have been some studies on the estimation of the trans-

mission shaft torque and the clutch pressure. The sliding modeobserver in [5] is designed to estimate the torque of an automo-

tive driveshaft in [6]. The adaptive sliding mode algorithm isproposed to estimate the turbine torque of a torque converter in[7]. Furthermore, [4] uses the sliding mode method to estimate

the clutch pressure in a hydraulically powered stepped AT. Theextended algorithm in [8] is used to estimate the clutch pressure

and the transmission output shaft torque simultaneously. In [9]and [10], a neural network is suggested to estimate the turbinetorque, in which the engine speed, the turbine speed, and the oiltemperature are inputs. [10] also designs a driving load observer

by assuming that the driving load is slowly-varying. In [11], re-cursive least square method with multiple forgetting factors isused to estimate the road grade and the vehicle mass. In [12],

a full-order observer is proposed for the pressure monitoringof a torque converters lock up clutch, where a state-dependentterm is appended in the conventional Luenberger state observer

to eliminate the effect of possible parameter variations in somesense. How to design this term is crucial for the performanceand the implementation of the observer.

A new AT with clutch-to-clutch shift technology is con-

sidered in this paper, in which electro-hydraulic actuators areadopted to control the clutches. A reduced-order clutch pres-sure observer based on the concept of input-to-state stability(ISS) [13], [14] is proposed, where the rotational speeds are themeasured outputs and the special structure of the clutch pres-

sure system is exploited. Model uncertainties are considered asadditional disturbance inputs. Lookup tables of the nonlinearcharacteristics of powertrain systems appear in their original

form. A systematic procedure is given to design the nonlinearclutch pressure observer such that it follows.

The error dynamics is ISS, where modelling errors are as

the inputs. This means that the initial estimation error de-

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GAO et al.: REDUCED-ORDER NONLINEAR CLUTCH PRESSURE OBSERVER FOR AUTOMATIC TRANSMISSION 447

Fig. 1. Schematic graph of automatic transmission.

cays exponentially and the estimation error is guaranteed

to be bounded for the bounded modelling errors. The requirements on estimation performance, such as

decay rate and error offset, are easily and explicitly con-sidered during the design process.

The implementation of the designed observer benefits from

the reduced order and the time-invariant gains of the ob-server.

Lower observer gains are obtained through convex opti-mization, which increases the robustness against noises

and reduces the estimated upper bound of the error offset.

The rest of this paper is organized as follows. In Section II, adynamic model of the considered transmission is derived for the

shift process. In Section III, the proposed reduced-order clutchpressure observer is described and the property of the observeris investigated in the concept of ISS. Based on the theoretic anal-ysis, a systematic procedure to design the pressure observer isthen given, following by a design example. In Section IV, the

proposed observer is tested on a complete powertrain simulationmodel, and comparison results with an existing sliding mode al-gorithm are given as well.

II. CLUTCH SYSTEM MODELLING AND PROBLEM STATEMENT

We consider the powertrain in passenger vehicles with a two-speed AT, as schematically shown in Fig. 1. A planetary gear

set is adopted as the shift gear. Two clutches are used as the

actuators. Two proportional pressure valves are used to control

the two clutches respectively. When clutch A is engaged and

clutch B disengaged, the powertrain operates on the 1st gear

and the speed ratio is given out by , where is the

ratio of the teeth number of the sun gear to that of the ring gear.

While clutch A is disengaged and clutch B engaged, the vehicle

is driven on the second gear with a speed ratio of .

During the shift process, the oncoming and offgoing clutches

are controlled by the two valves through a separate controller,

which is assumed to be well-designed. The power-on first to

second up shift is considered here as an example. The gear shiftprocess is divided into the torque phase where the turbine torque

is transferred from clutch A to clutch B and the inertia phase

where clutch B is synchronized [15].

By selecting the turbine speed , the speed difference of

clutchB , and the pressureof cylinder B asstatevariables

, respectively, the inertia phase of the first to second

gear up shift process is described in the following state space

form:(1a)

(1b)

(1c)

with

(2a)

(2b)

where is the current of valve B; is the turbine torque

[16], [17]; is the equivalent resistant torque delivered from

the tire to the drive shaft; is the constant coefficients de-

termined by the inertia moments of vehicle and transmission

shafts; denotes the return spring force of clutch B. Other pa-

rameters are defined in Table I and referred to [1] for the detailed

modeling process. In order to estimate the pressure of clutch B,

the rotational speeds of the transmission are used as the mea-

sured outputs, i.e., .

Similarly the torque phase of the first to second gear up shiftprocess is described in the following state space form:

(3a)

(3b)

with the measured output and

(4)

Note that are different coefficients from in (2). More-

over, although there is no obvious change of clutch speed duringthe torque phase, the speed difference of the clutch is dif-

ferent for various driving maneuvers. Hence, is also consid-

ered as an input for the torque phase model.

Due to the extreme complexity of the torque converter and

the aerodynamic drag, the nonlinear functions in (2) and (4) are

generally given as lookup tables, which are obtained by a series

of steady-state experimentations and contain inherently errors.

Other modelling uncertainties include variations of parameters,

such as the vehicle mass, the road grade and the damping coef-

ficient of shafts.

Hence, the problem considered in this paper is to estimate the

pressure of clutch B in the presence of model errors, given the

valve electric current , and the measured rotational speeds ofthe transmission , , and .



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TABLE IPARAMETERS FOR OBSERVER DESIGN

and torque converter, is represented in the form of lookup tables,

which is easy to be processed in computer.

According to Theorem 1 and Remark 3.1, a systematic proce-

dure is given to design the reduced-order nonlinear clutch pres-

sure observer in the form of (16) and (17) as follows:S1) choose the parameter according to the required decay

rate of the estimation error;

S2) choose the parameter , where it is suggested to start

from some smaller values (according to Remark 3.1);

S3) determine the observer gain such that (11) is satisfied;

S4) for a given model error bound, use (15) to compute the

estimated upper bound of the offset and check if the

offset bound is acceptable;

S5) if the offset bound is acceptable, end the design proce-

dure. If not acceptable, go to S2.

It is well known that getting model error bounds is in general

very difficult, if not impossible. As mentioned in Remark 3.2,

for a given model error bound, (15) gives just an upper boundof the estimation error offset, which might be much larger than

the real offset. Hence, the stop of iterating S1-S5 is somehow a

rule of thumb.

We now give a solution of (11) for choosing to be time-in-

variant, where the requirement for low observer gains can be

considered through optimization. If and in (11)

vary in a polytope with vertices, i.e.,

(18)

where denotes the convex hullof the polytope. Then, there

exist such that

(19)

with being functions of and satisfy

(20)

Hence, conclusions are given as follows.

Theorem 2: Suppose that and vary in a poly-

tope as (18). Then, any time-invariant satisfying the following

linear matrix inequalities (LMIs):

(21)

meets the observer gain condition (11).

Proof: The substitution of (19) into (11) leads to

(22)

Due to (20), the satisfaction of (21) guarantees (22) and hence

(11).

In (21), and are known and bounded, and

are selected to bounded and (where is the number

of time-varying parameters in and ) is bounded,

too. Hence, some constant is always found to render it sat-

isfying. Moreover, we prefer to low observer gains, for beingrobust against noises and reducing the upper bound of the error

offset, which is estimated by (15). Hence, is obtained through

the following convex optimization:

subject to LMIs (21) and (23)

Given and , the solution of (23) gives then a constant ob-

server gain with the lowest possible gains satisfying the condi-

tion (11).

C. Observer Design for Clutch Pressure

The inertia phase is taken as an example to show the detaileddesign procedure. The parameters are regarded as

constants for simplicity, which are listed in Table I, together

with the other parameters. Nonlinear functions , and

are given as lookup tables in the observer. These parameters

are derived from the nominal setting of an AMESim simulation

model of the AT shown in Fig. 1, which will be discussed later

in Section IV-A .

Following the procedure given in Section III-B , is chosen

to meet the requirement for the desired decay rate of the esti-

mation error. It is desired that the error converges in 0.1 s, and

the settling time as 4 time constants [18, p. 221], which implies

and results in .

Then, is chosen with the purpose of achieving a smalleroffset of the estimation error. Start with and obtain



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TABLE IIVALUES OF SIMULATION MODEL PARAMETERS

parameters ofthe proportionalpressure control valve

as constant, and we also ignore the time-delay of the valve. Ac-tually, the valve has a time-delay and the parameters vary ac-

cording to different operating points [19]. Hence, the dynamics

of the proportional valve in the powertrain simulation is given

by

(25)

Finally, the values of the parameters used in the power-

train simulation are listed in Table II. Nonlinear functions

, , , , and are given

as lookup tables.

B. Simulation ResultsThe proposed clutch pressure observer is programmed using

MATLAB/Simulink and combined with the above complete pow-

ertrain simulation model through cosimulations. The two clutch

valves are controlled by a predesigned clutch slip controller to

ensure a rapid and smooth shift process.

In this study, the major concern is put on the power-on first

to second gear up shift process. Left plots in Fig. 3 give the

simulation results of the shift process with the driving condition

of Table II, i.e., the condition for the observer design. During the

shift process, the engine throttle angle is adjusted to cooperate

with the transmission shift.

In both torque and inertia phases, the pressure of cylinder Bis estimated by the designed observers. After the inertia phase

(after 8.34 s), because the clutch B has been locked up, the pres-

sure is computed by the simplified control valve dynamics (1c).

During the torque phase (between 7.7 and 7.94 s) the rota-

tional speeds do not change greatly, whereas during the inertia

phase (between 7.94 and 8.34 s), the rotational speeds change

intensively because of the clutch slip. Hence, the estimation per-

formance in the inertia phase is much better, although it is also

acceptable in the torque phase. The estimation error is plotted in

the bottom of Fig. 3 as the solid line, where the result for

is also given as a comparison. The performance is improved.

The error peak is reduced about 35% and the average error is

reduced about 31%. Note that the shift process operates in thenominal driving condition, but the stiffness of the drive shaft and

the tire slip are considered in the simulation model, while these

are ignored in the model for designing the observer. Moreover,time-delay in control and time-varying parameters are also con-

sidered in the simulation model of the proportional valve.

The proposed observer is now tested under the driving condi-

tions which deviate from the nominal setting, where the vehicle

mass, the road grade, torque characteristics of the engine and the

torque converter are varied. The result with the relatively large

error is shown in the right of Fig. 3, where the driving condition

setting is as follows: the torque characteristics of the engine is

enlarged by 15%, and subsequently the capacity of the torque

converter is also enlarged; the vehicle mass is increased from

1500 to 1725 kg, and the road grade angle is varied from 0 to

5 deg.

Due to the large model errors, the pressure estimation errorbecomes larger in the torque phase. The reason is that there is

no slip in clutch A during the torque phase, and no large change

of the transmission speeds for the large vehicle inertia. There-

fore, the torsion of the drive shaft and the tires slip play im-

portant roles in the drive line. Omission of these terms in the

observer design deteriorate the estimation performance. In the

inertia phase, because of the clutch slip, the designed observer

still works well and the pressure estimation error is acceptable.

As a comparison, a full-order sliding mode observer is de-

signed according to [4], [6], given as follows:

(26a)(26b)

(26c)

for the inertia phase, and

(27a)

(27b)

for the torque phase. The sampling frequency of the sliding

mode observer is chosen to be 100 Hz, in order to test the

feasibility of the resulting observer for real applications [10].In the discrete implementation, the observer gains have to be



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452 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 18, NO. 2, MARCH 2010

Fig. 3. Left: Results of the nominal driving condition. Right: Results of different driving condition.

TABLE IIIGAINS OF DISCRETE OBSERVERS

reduced in order to restrain oscillations resulting from sam-

pling and two sets of the tuned values are given in Table III.

Hence, the proposed ISS observer is also discretized by the

same sampling frequency and the tuned gains are also listed in

Table III.

The comparison results of these three observers are shown in

Fig. 4, where the driving condition is the same as that in the

right of Fig. 3. In Fig. 4, the solid line represents the error ofthe reduced-order observer, while the dotted and dashed lines

represent the error of the full-order sliding mode observers with

the large and small gains, respectively. It is seen that the pro-

posed reduced-order observer works well in the inertia phase.

The sliding mode observer with large gains (Sliding 1) tracks

true values without large errors but with chatters, while the other

sliding mode observer (Sliding 2) achieves few chatters at the

cost of the large estimation errors. In the viewpoint of robust-

ness, the proposed observer achieves robustness in the sense ofISS, where the model errors are represented as external inputs.



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Fig. 4. Comparison between ISS observer and sliding mode observer (torqueconverter capacity is enlarged by 15%; m = 1725 kg; = 5 ) .

V. CONCLUSION

A reduced-order observer of clutch pressure is proposed in

the concept of ISS, for a new kind of AT adopting clutch-to-clutch shift technology. The proposed observer uses the easily

and precisely measured rotational speeds to constitute the cor-

rection term, while the lookup tables include the nonlinear char-

acteristics of engine systems. Hence, it is easily implemented.

Model uncertainties including steady state errors and unmod-

elled dynamics are considered as additive disturbance inputs

and the observer is designed such that the error dynamics is

input-to-state stable.

The designed observer is tested on an AMESim powertrain

simulation model, which contains the complete drivetrain and

clutch actuators. Simulation results show that, in the inertia

phase, the estimation error is restricted in the required boundeven if the model error is large.Comparing with thesliding mode

observer, the proposed ISS observer has the better estimation

performance and ability in eliminating chatters. In the torque

phase, however, the peak error becomes larger when the driving

condition deviates from the nominal setting. This deficiency

might be improved by using more precise models for the ob-

server design, such as a model in consideration of the torsion

of the drive shaft. It is going to be one of our future works.

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A Reduced-Order Nonlinear Clutch Pressure Observer for Automatic Transmission-T3Q

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