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54 Recent Patents on Electrical Engineering 2009, 2, 54-64

1874-4761/09 $100.00+.00 © 2009 Bentham Science Publishers Ltd.

Recent Patents on Measurement and Estimation in Brake-By-Wire Technology

Reza Hoseinnezhad§ and Alireza Bab-Hadiashar*

Faculty of Engineering and Industrial Sciences, Swinburne University of Technology, John Street, Hawthorn, Victoria 3122, Australia

Received: July 29, 2008; Accepted: October 27, 2008; Revised: November 13, 2008

Abstract: Similar to the fly-by-wire technology in aerospace industry, drive-by-wire technology in automotive industry

replaces the traditional mechanical and hydraulic systems with mechatronic actuation and control and human-machine

interfaces (such as pedals and steering wheels) with emulators. Brake-by-wire in particular, represents the replacement of

traditional components such as the pumps, hoses, fluids, belts and brake boosters and master cylinders with electronic

sensors and actuators. Some x-by-wire technologies have been already installed on commercial vehicles such as steer-by-

wire, and throttle-by-wire. Due to its safety-critical nature, the brake-by-wire technology is still under an intensive

research and development by some automobile and automotive parts manufacturers worldwide and has not been widely

commercialized yet. This paper surveys some recent patents that suggest techniques to solve two challenging problems in

brake-by-wire systems. The first problem involves reliable measurement of the driver’s brake demand using a multi-

sensor brake pedal mechanism and handling its missing data samples. The second challenge is the measurement or

estimation of the clamp force and actuator position/speed in a brake caliper for the purpose of actuator control. After an

overview of relevant recent patents, the current development trends as well as suggestions for key future developments are

presented.

Keywords: Brake-by-wire, electromechanical brakes, drive-by-wire, nonlinear observer, actuator control, position sensors, speed sensors, force sensors, vehicle stability control.

INTRODUCTION

Drive-by-wire, in general, refers to a series of recent automotive systems that allow the standard vehicle control tasks such as steering, throttling and braking, to be carried out by mechatronic components rather than the traditional mechanical methods. In brake-by-wire cars, the brakes are actuated and controlled by electric actuators and processors rather than by traditional hydraulic systems. This eliminates all brake fluids and pipes as well as most mechanical parts customarily associated with brakes. Brake-by-wire systems are also called electro-mechanical brakes (EMBs) and they are considered easier to manufacture and maintain, and are better for the environment.

The fly-by-wire technology was originally developed and used in the aerospace industry, where all flight control operations are carried out by mechatronic systems [1]. Eliminating bulky mechanical systems and replacing them with mechatronic systems created airplanes that were more reliable as well as more compact. Especially, using brake-by-wire technology allowed airplanes to be more fuel efficient as the removal of mechanical brake systems reduced the overall weight of the aircraft and cut out brake drag. This also had the added benefit of making brake pads last longer.

Using EMBs in cars also has several advantages. Proper deployment of EMBs are expected to eliminate the need for complex and heavy mechanical or hydraulic parts, to

*Address correspondence to this author at the Faculty of Engineering and

Industrial Sciences, Swinburne University of Technology, John Street,

Hawthorn, Victoria 3122, Australia; Tel: +61-3-9214-8526; Fax: +61-3-9214-8264; E-mail: [email protected] §Dr Reza Hoseinnezhad is currently with the Department of Electrical &

Electronic Engineering at the University of Melbourne, Victoria, 3010,

Australia.

enhance the efficiency and stability of brake control due to fast and accurate generation of brake torques by electric motors, and to improve diagnostic capability of the braking system. On the other hand, the design and development of brake-by-wire systems are challenging tasks because they involve a complete change in requirements from previous hydraulic and electro-hydraulic braking systems. Parti-cularly, due to safety-critical nature of brake systems, having high reliability in terms of being tolerant to hardware (sensors, actuators and processors) and software faults is crucial which in turn makes the development task especially challenging.

A general diagram of a brake-by-wire system is shown in Fig. (1) which includes four principal components: a central brake controller, a sensing/measurement apparatus for driver’s brake demand, brake units in four corners of the vehicle, and a communication network.

The central brake controller (which in some cases is embedded within the vehicle’s Electronic Control Unit - ECU) is responsible for generating brake commands to implement high level stability control systems such as antilock braking system (ABS), traction control (TC), vehicle stability control (VSC), electronic stability protocol (ESP) and the like. Since the stability control systems for electro-hydraulic brakes have already been developed, the major challenge for EMB systems has been the proper adaptation of those techniques for an EMB system. This has been an active area of research and a number of novel methods and techniques have appeared in brake-by-wire patent literature [2-5].

The communication network is responsible for trans-mitting signals between the different sensors, processors and actuators in the system. Because of safety critical nature of

Measurement and Estimation in Brake-By-Wire Recent Patents on Electrical Engineering, 2009, Vol. 2, No. 1 55

the system, the network needs to be fault tolerant. For this purpose, a number of redundant time-triggered or event-triggered communication networks have been suggested in the brake-by-wire patent literature [6-8].

The brake request sensing apparatus is what appears as the brake pedal in conventional braking systems. In a brake-by-wire system, this apparatus is not necessarily a pedal. For instance, it can be a device adjacent to the driver’s hands on the steering wheel, enabling the driver to apply brakes with minimal hand movements [9]. However, a pedal shape for the apparatus (e.g. the brake-by-wire pedal architecture suggested in [10]) seems to be more widely accepted by the drivers, and hereafter in this paper, the apparatus will be referred to as the brake-by-wire pedal or the pedal for short. A brake-by-wire pedal is usually equipped with several sensors which provide redundant information about the driver’s brake request. Those sensory information need to be integrated to result in reliable (fault tolerant) measurements of the driver’s brake request. In case of similar sensors, the integration is usually called voting, and for dissimilar sensors, sensor fusion techniques are used.

Upon measurement of the driver’s brake request, the brake demand is sent to the central brake controller via the communication network. Based on the ABS, TC, ESP or VSC rules implemented in the central brake controller, the controller generates four independent brake commands and sends them to the four brake units. Those commands are usually in the form of four desired clamp force that should be generated between each of the four brake discs and their corresponding brake pads. Each brake unit includes a caliper and an actuator to clamp the brake pad toward the disc. There are also some sensors and a controller to tune the actual brake force to the desired clamp force received from

the central brake controller. Henceforth in this paper, this controller will be referred to as the caliper local controller or local controller for short. In order to tune the brake pad-disc clamp force according to the commands sent by the central brake controller, a caliper local controller needs to measure and feedback the actual clamp force (closing the control loop).

Measurement of the driver’s brake demand (by integration of pedal sensory information) and clamp force measurement/estimation are two challenging problems that have been addressed in several patents during recent years. This paper reviews those patents and other recent developments in measurement and estimation of the driver’s brake request and the generated clamp force.

In section II, most recent patented techniques for measurement of driver’s brake request are reviewed. Since the data samples associated with the driver’s brake request are safety critical information in a brake-by-wire system (the system is of no usage without driver’s command and the loss of such data may have catastrophic results), a novel tech-nique to handle the missing data samples and compensate for such samples is also reviewed in section III. Section IV presents a brief description of recent techniques for measurement of local control of the disc-pad clamp force in each caliper of an EMB. Since most of the clamp force estimation/measurement techniques reviewed in this section need an accurate measurement of brake actuator (motor) position and/or speed, a novel patented technique for this measurement is also described in section V. This paper concludes with a short discussion on current trend and future developments toward locally smart EMB calipers as presented in section VI.

Fig. (1). General structure of a brake-by-wire system [15].

56 Recent Patents on Electrical Engineering, 2009, Vol. 2, No. 1 Hoseinnezhad and Bab-Hadiashar

MEASUREMENT OF DRIVER’S BRAKE REQUEST

Since all brake actions are determined based on the driver’s brake request, the accurate measurement of brake demand is a high priority task in any brake-by-wire system. To accommodate for sufficient fault tolerance, a brake-by-wire pedal is usually equipped with several sensors providing redundant information regarding the driver’s brake request.

The number and types of pedal sensors are dependent upon the pedal design itself. An existing design of a pedal unit currently includes one force and one displacement sensors [11]. As it is shown in the schematic diagram of Fig. (2), in this design the signal from the displacement sensor, which measures the pedal movement, is provided to a first (master) controller and the signal from the pedal force sensor (measuring the pressure exerted by driver’s foot on the pedal) is provided to a second (slave) controller. The two controllers each receive data from one sensor and commu-nicate via a bi-directional link.

In one of the controllers (presumably the master), the force and displacement signals are combined for the generation of brake command signals that is sent to brake units. This combination is designed in a fault tolerant manner, so that if any of the controllers does not receive its corresponding sensor signal, the brake command signal is nevertheless generated on the basis of the existing sensor signal.

The force and displacement data are first converted to their “compensated form” by linearly scaling each signal to a value within its full variation range or by using a lookup table. The two compensated signals are then combined by simply taking the larger compensated signal as the sensory measurement of the driver’s brake command. This design is fault tolerant in the sense that in case of failure of one of the sensors, failure of the communication link, or failure of one of the two controllers, the other controller will use the available signals to provide braking operation according to either brake pedal travel or applied force.

In the above scheme, there is an underlying assumption that a faulty sensor would generate a zero signal or a small noise, and therefore, the signal of the other sensor will be always larger in its scaled size. However, this assumption is not always correct. For example, a short-circuit to supply voltage may cause a high signal from a faulty sensor.

In an alternative design suggested in [12], the brake pedal includes three sensors. Two sensors are of similar type (e.g. force sensor) and the third is different (e.g. displacement sensor). The third sensor does not necessarily have a high resolution as it is included merely to increase the fault tolerance of the measurement system by detection of a fault in either of the other two sensors and removing the faulty sensor from the brake demand measurement using a voting method. A simple voting process has been suggested in [12] and is presented in a diagram shown in Fig. (3). Two force signals (F1 and F2) and one displacement signal (s) are three inputs to the voting process.

The displacement signal is converted to an equivalent force reading, F3, using a lookup table or function f1. There are two constant parameters 1 and 2 which are used as comparison thresholds in the voting scheme. The outputs of the voting scheme include the driver’s brake requirement Fw and a single bit alarm for sensors malfunctioning.

The difference (disparity) between the two force sensors, |F1-F2|, is first measured and compared to the threshold 1. If the disparity is smaller than the set threshold, the two sensors agree and their average value is returned as the driver’s brake request Fw.

When the two force sensors agree, a fault in the third sensor is detected by comparing its output with the first two sensors. If the position sensor does not agree with either of the force sensors, i.e. either of the two disparities |F3-F1| or |F3-F2| are larger than the threshold 2, the sensors malfunctioning alarm is then activated.

If the two force sensors disagree, i.e. |F1-F2|> 1, then the sensors malfunctioning alarm will be activated and the signal of the position sensor is utilised to detect the faulty sensor as

Fig. (2). A brake pedal unit with one force and one displacement sensor [11].


follows: If |F1-F2|> 1 and |F3-F2|>|F3-F1| then F2 is considered faulty. If |F1-F2|> 1 and |F3-F1|>|F3-F2| then F1 is considered faulty.

In a similar work [13], the authors have developed a

fuzzy voting technique for integration of the three sensors

measurements and detection of a faulty sensor. A block

diagram of our fuzzy voting method, as applied to fuse the

three pedal sensory measurements, is shown in Fig. (4). S1

and S2 are the two force sensors giving f1 and f2 signals, and

S3 is the displacement sensor with its signal denoted by x.

The pedal displacement signal is converted to equivalent

force signals and and compared with the signals

provided by the other two sensors. In order to perform this

conversion, a model is required to mathematically relate the

three signals x, f1 and f2. The following models have been

suggested in [13] for conversion of displacement mea-

surements to equivalent force values:

f1 = A1x

2+ B1x + C1 + D1x

2+ E1x (1)

f2 = A2x2

+ B2x + C2 + D2x2+ E2x . (2)

Using the equivalent force values of the displacement measurement, the sensors agreements are then evaluated by computing the following differences between their measurements:

12 = f1 f2 ; 23 = f2 f2 ; 31 = f1 f1 . (3)

The usual “hard” voting methods, such as the technique suggested in [12], detect a sensor as faulty and remove it from calculations instantly. Our “soft” fuzzy voting method, on the other hand, is more flexible as it detects and removes a faulty sensor gradually and generates three status signals (instead of status bits) using a fuzzy inference engine.

A “hard” voter outputs a fused value and three status bits, showing the occurrence of faults in the sensors. These outputs are determined based on the results of comparing i,j values with an agreement threshold. For instance, if 1,2 and

1,3 are higher than a given threshold (i.e. neither S1 and S2 nor S1 and S3 agree with each other) and 2,3 is lower than the threshold (i.e. S2 and S3 agree with each other), then the hard voter will deduce that S1 is faulty. In this case, the fused output will be the average of S2 and S3 and the faultiness status bits will be 100 for S1, S2 and S3, respectively.

In case of sudden sensor failures, the hard and soft sensors perform similarly. However, the fault may be a signal drift or due to excessive noise. The case studies presented in [13] show that in such circumstances the hard voter detects the fault with a delay while the soft voter is capable of early detection of sensor drifts or excessive noise.

HANDLING OF MISSING DATA SAMPLES

The design of a by-wire car should provide safeguards against missing some of the data samples provided by the safety critical sensors or other important estimated values. For instance, the ECU should always be informed of the

Fig. (3). A three-sensor pedal suit and brake demand measurement [12].


Fig. (4). Block diagram of the pedal sensor fusion based on a fuzzy

voting scheme as suggested in [13].

driver’s intentions to brake or to stop the vehicle as the functionality of the vehicle control system relies on that information. Therefore, it is crucial that the sensory infor-mation regarding the driver’s brake request are not missed.

Besides a complete sensor loss, the ECU may also suffer an intermittent (temporary) data loss. For example, sensor data can sometimes fail to reach the ECU. This may happen due to a temporary problem with the sensor itself or with the data transmission path. It may also result from an instan-taneous short circuit or disconnection, a communication network fault or a sudden increase in noise. In such cases, for a safe operation, the system has to be compensated for missing data samples.

Popular solutions to compensate for missing data samples are based on providing redundant sensors and applying failsafe mechanisms in the drive-by-wire systems. In an alternative approach, the authors have suggested a new technique to handle the missing data samples based on continuously predicting the data samples and replacing the missing ones with their previously predicted values. In most cases, where multiple consecutive samples are missing, multi-step ahead predictive filters are needed [14,15].

Assume that in a by-wire system, a particular safety critical component or sensor is compensated for a maximum number of L consecutive missing data samples. Depending

on the number of recently missed samples, compensation of the sensor for its new missing sample may require a one-step, two-step, … or L-step ahead prediction. The predicted value of each missing sample is formulated as a linear combination of its recent P values, as follows:

y(k) = ai y '(k i)i=1

P

(4)

where y'(k-i) is either equal to the previous data sample y(k-i) or the previously predicted sample (k-i). Because of memory and computational time constraints, it is desired to tune the same ai values for prediction in all cases.

Figure 5 shows a block diagram for the multi-step ahead predictive filtering scheme. For each safety critical component in this system, a status bit accompanies the data stream. The status bit is reset if for any reason, the data sample is invalid and considered missing. The missing sample is then replaced by a linear combination of previous valid or missing (but predicted and replaced) samples.

As it is shown in Fig. (5), this scheme only requires to store the P constant weights of the filter and the recent P values of the compensated module’s output signal, no matter how many consecutive samples are missing. Also, in terms of computation, it only comprises two multiplications and P-1 summations.

In order to tune the ai weights in such a way that the resulting filter can optimally predict missing data samples of a signal, the following error function is optimised:

E =1

2Cj y(k) yj (k)

k=1

N 2

j=1

L (5)

where N is the total number of samples used for off-line tuning and yj is the signal predicted at the time k - j. The non-negative Cj constants are selected based on the given priority of the performance of one to L-step ahead predictions. This prioritisation depends on the application. To solve this non-linear least square problem, the gradient search method is used.

BRAKE FORCE ESTIMATION AND CONTROL IN BBW ACTUATORS

As shown in Fig. (1), each of the four brake units in a brake-by-wire system comprises four main parts: a brake caliper, an actuator, a local controller to regulate the brake force (to the clamping force commanded by the central brake controller) and a number of sensors or observers to measure some quantities (such as the speed, position or current of the actuator or the brake force) in the brake unit. Those measurements are vital to close the control loop and their accuracy and signal to noise ratio are of critical importance to guarantee the stability of the control systems.

Various configurations for a local controller have been suggested in the patent literature. In one embodiment suggested in [16] and depicted in the diagram shown in Fig. (6), the actuator is a brushless DC (BLDC) motor which is controlled via a power electronic inverter tuned by “off” and “on” conduction angles determined from a lookup table. The reference turn off and turn on angles, off and on are


determined in a closed loop using a “phase advance angle”

adv as follows:

adv = G e = G (Tpredicted - Tmax) (6)

where e is the closed loop control error. In this design, the central brake controller sends brake torque (instead of clamp force) commands to each caliper. The future torque com-mand at the next sampling time, Tpredicted is predicted by a predictive filter and compared with the maximum instan-taneous motor torque available, Tmax . This error is used to adjust the conduction angles in a manner that meets the anticipated torque requirements without unnecessarily increasing average motor current.

Using the phase signal advance angle adv , given by the error gain module, the conduction angles taken from a conduction angles lookup table stored in a ROM are updated as follows:

on (n+1) = on (n) – adv (n) (7)

off (n+1) = off (n) – adv (n). (8)

The predictive filtering scheme suggested in [16] is the following simple FIR filter:

Tpredicted = (n+1) = a0 T(n) + a1 T(n-1) + a2 T(n-2) + … + aL

T(n-L) (9)

and for the sake of computational simplicity and memory efficiency, a simple case involving L=1 , a0 =2 and a1 = -1

has been especially studied in [16]. An alternative approach is to use the adaptive predictive scheme [14,15] described in section III.

The maximum available instantaneous torque is calculated according to the following equation:

Tmax (n) = KT Vsup . Kv (n)( ) R (10)

where KT and Kv are the motor torque and voltage constants, R is the resistance of the motor phase coil, Vsup. is the supply voltage. The motor speed is evaluated, using position measurements of an incremental encoder, as follows:

(n) =(n) (n 1)

t (11)

where is the encoder’s measurement and t is the sampling time.

In the above torque prediction-based technique, as suggested in [16], the controller tunes the actual clamping brake force/torque to its maximum possible value. However, it appears that the other recently patented solutions for the local brake controller design problem are based on achieving a clamping force/torque that is as close as possible to the command sent from the central brake controller [17,18]. These controllers each include a closed-loop in which the clamp force/torque is measured or estimated and fed back to the controller. For example, in [17], an observer is suggested for estimation of the clamp force using motor voltage,

Fig. (5). Block diagram of a multi-step ahead predictive filtering as a compensation scheme for missing data samples [14,15].

Fig. (6). A schematic diagram of the actuator controller design suggested in [16] for brake-by-wire calipers.


current and speed measurements. The linear observer is the dynamic system expressed with the following set of equations:

dI

dt=

1

LV RI Kv( ) + KO (I I ) (12)

Tobserved = KT I J c (13)

where V, I and are the motor voltage, current and speed, respectively, Î is the state of the observer (and an estimate for the current) and Tobserved is the estimated brake torque. The parameters L, J and c are the inductance of the motor phase coil, and the total inertia and damping coefficient of the motor, respectively. The parameter Ko is the observer gain. The motor speed and its time-derivative are calculated using equation (11).

As it is shown in Fig. (7), the estimated brake torque is compared to the torque command sent by the central controller to the caliper. The control error signal is then inputted to a PID controller which feeds the power electronic components to drive the motor.

The above technique estimates the brake torque as a linear dynamic function of current, speed and acceleration of

the motor. In alternative approaches, control loop is closed by nonlinear estimators. For instance, in an implementation suggested in [18], the brake force is estimated using an adaptive lookup table as shown in Fig. (8). This lookup table employs a nonlinear relationship between the brake force and the motor position which is called the characteristic curve of the brake caliper.

The characteristic curve of a brake-by-wire caliper (also called electromechanical brake caliper) varies with ageing and temperature [19,20]. Figure 9 shows an example of how the characteristic curve can change due to ageing and temperature. The characteristic curve variations should be considered in any estimator that uses a representation of this curve (e.g. a lookup table or a polynomial representation). Thus, the representation needs to be adaptive and constantly updated.

It is important to note that in the controller design suggested in [18], a brake force sensor is assumed to be available. However, the force sensor is usually realized in the form of a load cell bridge shown in Fig. (10) and with such an arrangement, the force signal typically has a low signal to noise ratio and therefore may require significant filtering, resulting in a slower response time and reduced performance.

Fig. (7). Actuator torque controller design using a linear torque observer as suggested in [17].

Fig. (8). Estimation of brake force as a nonlinear function of the actuator’s position implemented by an adaptive lookup table as suggested in

[18].


The local controller design, shown in Fig. (8), presents a solution to this problem. As explained in [18], the solution involves real time estimation of the force from actuator position measurements and using the force sensor measurements for the purpose of adaptation of the brake force observer (lookup table). The lookup table comprises an ensemble of n data points with position and force components {xi , fi}; i = 1 , 2 , … , n. For each position measurement x, a force estimate is given as the following weighted average of all force coordinates of the points in the lookup table:

fest = wi (x) fi

i=1

n

wi

i=1

n

(x) (14)

where the weights wi are functions of the position coor-dinates xi and measurement x. A triangular weight function is given by:

wi =1 x xi if x xi 1

0 else (15)

and a Gaussian profile is given by:

wi = expx xi

2

(16)

where is the bandwidth of the triangular or Gaussian profiles as shown in Fig. (11).

(a)

(b)

Fig. (11). Two examples of weight function profiles to be use in the

adaptation scheme suggested in [18]: (a) Triangular profile (b)

Gaussian profile.

According to the adaptation method suggested in [18], for each position and force measurements, x and f, the force estimation error e = f - fest is calculated and each fi coordinate in the lookup table is replaced with fi+ e wi. Indeed, the coordinates change in the inverse direction of the gradient of the cost function J = e2 to minimize this function and hence, minimize the estimation error.

(a) (b)

Fig. (9). Variation of the static force-position curve (called the characteristic curve) of a brake-by-wire caliper: (a) with different brake pad

thicknesses, and (b) in different temperatures.

Fig. (10). Arrangement of four strain gauges in each load cell around the load sleeve of the actuator and the strain gauge electrical circuitry.


Another issue with the clamp force measurements, using the load cell arrangement shown in Fig. (10), is hysteretic nature of, variation of clamp forces with actuator displacement in dynamic cases. To resolve this issue, a practical solution is suggested in [19]. It is a simple and memory-efficient real-time calibration technique in which a clamp force model (a Maxwell-slip model for the hysteresis caused by friction) is fitted to the data samples around each hysteresis cycle.

We have also introduced a dynamic and nonlinear model for clamp force estimation in brake-by-wire systems [20]. However, for the sake of completeness, a similar patented work [21] is also discussed here. In this technique, the clamp force is estimated by an observer using the following nonlinear dynamic equation:

fest = kl x + knl x2+ dm m / N (17)

where x is the actuator (or brake pad) displacement, m is the motor speed (can be calculated from equation (11) using motor position measurements), N is the total gear ratio (ratio of actuator displacement to the motor displacement), and kl , knl and dm are the coefficients of linear and nonlinear terms, and damping coefficient, respectively.

As it is shown in Fig. (12), the constant parameters in equation (17) are occasionally updated using a parameter identification scheme. A status bit controls the timing of parameter identification, e.g. the updating may occur only upon key insertion. During the update, an apply-with-dither test is run to adaptively identify the new parameters using previously stored values as initial conditions. The updating scheme may be only offline (performed only in service times) or occasional (whenever the ignition key is inserted) or online (during any braking action).

During each apply-with-dither test, the parameters of the brake force observer given by equation (17) are adaptively updated as follows:

kl = pJmN 2 ( ˆ

m m ) m0 (18)

knl = pJmN 3( ˆ

m m ) m02 (19)

dm = pJm m ( ˆ

m m ) . (20)

The constant p is a positive adaptation gain, Jm is the motor inertia and m0 is the initial motor angle (at the beginning of the parameter identification phase). It is important to note that for each sample of known force and displacement values, f and x, for a given set of parameters, {kl , knl , dm}, a motor speed estimate can be derived using the following equation:

ˆm = N f kl x knl x

2( ) dm (21)

and equations (18)-(20) translate the error of the above speed estimator to adaptation rules for the three parameters.

MEASUREMENT OF BRAKE ACTUATORS POSITION AND SPEED USING RESOLVERS

The review presented in section IV shows that all recently patented techniques for brake force estimation and control rely on accurate measurement of actuator (motor) position and speed. There are a number of critical issues with usage of incremental encoders for this purpose, as they are relative position sensors and their additive errors need to be calibrated or compensated for.

An alternative option is to use resolvers to provide accurate and continuous measurements for both absolute position and speed of the rotor of the actuators. Unlike the encoders, resolvers provide two output signals that always allow the detection of absolute angular position. In addition, they suppress common mode noise and are especially useful in a noisy environment.

Fig. (12). Estimation of the brake force by a dynamic nonlinear observer which is updated using a parameter identification scheme, as

suggested in [21].


A resolver is a rotary transformer with one rotating reference winding and two stator windings. The reference winding is fixed on the rotor and rotates jointly with the shaft passing the output windings. Two stator windings are placed in quadrature of one another and generate the sine and cosine voltages (Usin and Ucos). The sine winding is phase advanced by 90 with respect to cosine winding. In consequence of the excitement applied on the reference winding and along with the angular movement of the motor shaft m, the respective voltages are generated by resolver output windings Usin and Ucos. The amplitudes of the generated voltages vary according to the sine and cosine of the shaft angle m. There are also phase shift and amplitude balance parameters that need to be calibrated before using the sensor for position measurement [22].

In a trigonometric approach, the shaft angle is determined by an inverse tangent function of the quotient of the sampled resolver output voltages Usin and Ucos given by the following equation:

ˆm = arctan

Usin

Ucos

. (22)

Brake force control techniques require knowledge of both the motor angle and its speed. The trigonometric method, however, only yields estimates of the unfiltered rotor angle without any speed information. Therefore, for a final application, a speed calculation such as the difference equation (11) should be added. Furthermore, the four-quadrant inverse tangent results in angles between -180 and 180 . Thus, the number of turns (required for position control) is not tracked.

We have devised a hybrid Angle Tracking Observer (ATO) based on counting the quarter of turns using a quadrature encoder and integrating those with the outputs of a closed loop observer [23, 24]. The quadrature encoder

includes two Schmitt triggers that trigger their states at zero crossing points of the Usin and Ucos signals. It also includes a counter to count the zeros crossing points. Disregarding the noise and the narrow hysteresis band of the Schmitt triggers, the output of the encoder is expressed by the following function of the rotor angle:

quad =2

2m +

4 (23)

where . means “rounding to the nearest integer towards minus infinity.” Due to its discrete nature, the output of the quadrature encoder is robust both to the additive noise in the resolver sinusoidal outputs and the hysteresis band of the Schmitt triggers.

The block diagram of our proposed ATO is shown in Fig. (13). The symbol |.| means “absolute value” and the “sign” block returns 0 or 1 for negative or nonnegative arguments, respectively. M is a constant switching threshold. The input signal to the open loop observer (i.e., G(s)/s) is an error signal ê(t) given by the following equation:

e(t) =

sin m (t) ˆm (t)( ) if quad (t) ˆ

m (t)

quad (t) ˆm (t). else

(24)

The absolute position and speed of the rotor are the outputs of a closed-loop observer. The error signal that is fed back to the input of the feed-forward path initially equals

sin m (t) ˆm (t)( ) . However, if the observer shows its

tendency to diverge, this error signal is switched to another signal, which is the difference between the output angle estimate and the quadrature encoder output. This difference is continually calculated by the ATO, and the switching is carried out if absolute value of this difference is greater than the threshold M (alarming that the observer is tending toward

Fig. (13). Schematic diagram of the hybrid Angle Tracking Observer (ATO) based on counting the quarter of turns using a quadrature

encoder and integrating those with the outputs of a closed loop observer [23,24].


divergence). Using nonlinear control systems theory, we have devised design formulas for open loop transfer function G(s)/s in such a way that robust stability of the above closed-loop nonlinear observer is guaranteed.

CURRENT & FUTURE DEVELOPMENTS

In current trend of research and development on brake-by-wire, the best next step appears to be towards development of a smart EMB caliper. A smart caliper is characterised by its ability to maintain each wheel in a stable region while maximising the tire-road friction locally and independently. Currently, neither the cars in production nor any of the manufactured prototypes are able to perform ABS (via wheel slip control) locally in each corner. In all existing models, wheel slip control and ABS tasks are performed by the central brake controller (ECU).

The distributed nature of brake-by-wire design necessitates local ABS routines to be implemented in each corner. In brake-by-wire calipers, numerous sensors and observer modules provide a wealth of local information (e.g. clamp force, actuator current, position, and speed and temperature) that can be utilised to accurately estimate the tire-road friction in real time which in turn can be used to achieve a corner-based ABS regime.

As the next step for future development of brake-by-wire systems, we propose a research on design and implemen-tation of appropriate local estimators (or observers) and controllers to accurately quantify the tire-road friction in each corner of the vehicle independently and regulate the brake commands to maximise the friction. Such a corner-based control system should therefore be able to maintain each wheel in its stable region especially in extreme driving conditions. The main focus will be on devising techniques with low computational complexity (to be easily imple-mented in each WBCU) that only use locally available information (such as clamp force, actuator current and position and wheel speed) for precise estimation and control at each corner.

At the first stage, estimators (or observers) are needed to measure the tire-road friction by using locally available information including the clamp force, wheel angular speed and/or acceleration, and other signals such as actuator current and position or speed and caliper temperature. Such corner-based friction estimators should be accurate and very robust to ageing and environmental changes.

At the next step, a corner-based control strategy should be devised. The technique will use the estimated friction to regulate the brake command (in form of a desired clamp force) in such a way that maximum tire-road friction is maximised. Thus, the controller should guarantee to maintain the wheel state (speed, acceleration and slip) in the stable region. It should also be robust to ageing and environmental variations. The final stage will involve designing and imple-

menting experimental validation regimes that comprehen-sively test the developed techniques and are expected to include numerous test drives in various extreme driving conditions.

Brake-by-wire technology can be especially useful to be implemented on board the future generations of electric, hybrid and semi-hybrid vehicles, where the regenerative braking force can be utilized to increase the charge of the locomotion batteries.

ACKNOWLEDGEMENT

This research was supported by the Australian Research Council and Pacifica Group Technologies (PGT) through the ARC Linkage Project grant LP0561923.

CONFLICT OF INTERESTS

There is no current or potential conflict of interest to be provided.

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