real-time simulation of a network … · are read from a database container (dbc) file. ......

BULETINUL INSTITUTULUI POLITEHNIC DIN IAŞI Publicat de

Universitatea Tehnică „Gheorghe Asachi” din Iaşi Tomul LIX (LXIII), Fasc. 3, 2013

SecŃia AUTOMATICĂ şi CALCULATOARE

REAL-TIME SIMULATION OF A NETWORK-CONTROLLED VEHICLE DRIVETRAIN

BASED ON MODEL PREDICTIVE CONTROL

BY

CONSTANTIN F. CĂRUNTU*

“Gheorghe Asachi” Technical University of Iaşi, Faculty of Automatic Control and Computer Engineering

Received: September 1, 2013 Accepted for publication: October 15, 2013

Abstract. This paper proposes a real-time implementation of a networked predictive controller designed to damp driveline oscillations, which is crucial in improving drivability and passenger comfort, while compensating the time-varying delays that appear due to sending the control commands and the measurements from the sensors through Controller Area Network (CAN). Firstly, the designed real-time structure integrated with CAN test-bench is described and then the model of the drivetrain is derived. Secondly, considering that the CAN-induced time-varying delays are bounded, a method of modelling the physical plant (vehicle drivetrain) including the delays is proposed. Then, a predictive control strategy, which makes use of the previously developed model, is designed in order to damp the driveline oscillations. The proposed control scheme is tested using the designed test-bench and the experiments based on realistic scenarios show that the proposed controller can outperform classical controllers, e.g., PI.

Key words: real-time simulation, networked control systems, drivetrain oscillations damping, model predictive control.

2010 Mathematics Subject Classification: 93C10, 93D05, 00A72.

*Corresponding author; e-mail: [email protected]

Constantin F. Căruntu

44

1. Introduction

The testing of control algorithms in their real environment represents an important phase in the development of control units for drivetrains. The major feature of a real-time simulation is that the simulation can be carried out as quickly as the real system would actually run, thereby allowing to combine the simulation and the real Controller Area Network (CAN). The absence of the real device in the simulation particularly simplifies the development, since it allows systematically and effectively validation of the closed-loop control system, even if the controller is not hardware implemented.

The use of real-time simulation can replace significantly the construction of expensive prototypes to test drivetrain systems. Due to the utilization of the model in the closed loop, the simulation is cheaper, easier and less time consuming than experiments on the real device. Moreover, the risks to damage the real device are eliminated.

The computing power required by real-time simulation highly depends on the characteristics of the simulated model: if it contains very demanding calculations you have to provide a lot of computing power because the timing cannot be satisfied otherwise. A simulation performed in Simulink takes as much computer time as needed to compute the control command and then to calculate the behaviour of the system. If the model is complex, much more time is needed to carry out the necessary calculations. Because a strict time requirement for Simulink simulation does not need to be fulfilled, the complexity of the model does not need to be reduced.

Recently, the need for increased drivability and passenger comfort lead to the design of proper models for vehicle drivetrains and to the development of different control strategies to minimize the effects of drivetrain oscillations. When a vehicle is subjected to acceleration, the elasticity of the various components in the driveline may cause torsional vibrations or disturbances. The oscillations give rise, apart from material stress, to noticeable reduced drivability and comfort.

Various research has been carried on understanding and damping the driveline oscillations of a conventional vehicle and numerous control strategies were reported in the literature: robust pole placement (Richard et al., 1999), (Stewart et al., 2005), H∞ optimization (Lefebvre et al., 2003), linear quadratic gaussian control design with loop transfer recovery (LQG/LTR) (Petterson, 1997; Fredriksson et al., 2002; Berriri et al., 2008) and model predictive control (MPC) (Rostalski et al., 2007).

All of the above control solutions assume that the sensors, controllers and actuators are directly connected, which is not realistic. Rather, in modern vehicles, the control signals from the controllers and the measurements from the sensors are exchanged using a communication network, e.g., CAN or Flexray, among control system components. This brings up a new challenge on how to

Bul. Inst. Polit. Iaşi, t. LIX (LXIII), f. 3, 2013

45

deal with the effects of the network-induced delays and packet losses in the control loop. The delays may be unknown and time-varying and may degrade the performances of control systems designed without considering them and can even destabilize the closed-loop system.

As such, the problem considered in this paper is the development of a real-time simulation test-bench designed to validate a networked predictive control strategy proposed to minimize the oscillations of a vehicle drivetrain, while compensating the time-varying delays introduced by CAN. The proposed solution consists of three steps: firstly, a standard continuous-time model of the vehicle drivetrain is derived and a technique to model the physical plant including the network-induced time-varying delays is proposed; secondly, a predictive control scheme is presented and designed using the previously developed model. Thirdly, the networked predictive controller and the plant model are implemented on a real-time simulation test-bench and the designed experiments validate the proposed approach and indicate that the proposed scheme can outperform other types of controllers, e.g., PI.

The remainder of the paper is organized as follows. Section II illustrates the control architecture for the network-controlled vehicle drivetrain and then the real-time simulation setup is described. In Section III the mathematical model describing the vehicle drivetrain is introduced, including the modelling of the time-varying delays induced by CAN. In Section IV the predictive control strategy is presented and then the experimental results are discussed in Section V. The concluding remarks are given in Section VI.

2. Drivetrain Control Architecture

The structure of a vehicle drivetrain has following parts: engine, clutch, transmission, final drive, driveshafts and wheels, as it can be seen in Fig. 1.

Fig. 1 – Schematic vehicle drivetrain structure.

2.1. Network-Controlled Architecture

As it can be seen in Fig. 2 the components of the closed-loop vehicle

drivetrain control system, e.g., sensors, controllers, actuators, communicate


46

through a communication network, i.e., the engine torque (control signal), and also the engine and wheel speeds are sent through CAN. Many of the messages transmitted on CAN, have real-time constraints associated with them.

The complete network-controlled architecture considered in this paper, which is graphically depicted in Fig. 2, consists of the following operations:

− the sensor measures the output of the system and sends the sample to the controller through CAN;

− the cruise controller receives the measurement from the sensor and the desired velocity reference r r

v w wv r ω= and computes the required torque,

while handling the physical constraints and the delays, where wr is the wheel

radius and r

wω is the desired wheel speed; − the control signal, i.e., the torque computed by the controller, is sent

to the engine controller through CAN; − the engine controller actuates the spark timing and airflow (

au ) as requested for driveline control.

In Fig. 2 the dashed lines represent the direction of the messages sent to and from the controllers.

Fig. 2 – Drivetrain control architecture.

In the study case considered in this paper, the actuator is the internal

combustion engine and it is assumed that the request is accomplished instantaneously. For the engine torque this can be achieved by creating a torque reserve by changing the spark timing in order to have quickly available torque modifications (Di Cairano et al., 2010). The torque reserve can be maintained by appropriately actuating the fuel injection.

2.2. Real-Time Simulation Setup

The test-bench setup for real-time simulation of the closed-loop drivetrain control system, which is designed to validate the control algorithm, consists of: dSPACE MicroAutoBox, Host PC, CAN network, power supply, a Simulink model of the drivetrain and controller and a Graphical User Interface (GUI) as it can be seen in Fig. 3.


47

MicroAutoBox consists of the base board DS1401 and it performs the drivetrain plant-model and controller calculations. It has an Ethernet interface for direct connection to the host PC, for tasks such as loading models and reading or adjustig parameters in GUI. The HostPC also runs the simulator’s console software (GUI), designed in ControlDesk, to communicate with the real-time Simulator (MicroAutoBox). The application allows to download and control the real time process such as download, start, stop, variable observation and data collection.

Real-Time Interface (RTI) is the link between dSPACE MicroAutoBox hardware and the MATLAB/Simulink models. Real-Time Workshop (RTW) generates the model code which is compiled and downloaded in the real-time hardware. The RTI CAN MultiMessage Blockset is an extension for Real-Time Interface and it is used for configuring the CAN network. CAN configurations are read from a database container (DBC) file.

Fig. 3 – Real-time test-bench.

Note that the Simulink model of the drivetrain and controller exchange

data via a CAN bus. Also, the Host PC has access to the CAN bus through the developed ControlDesk application.

3. Drivetrain Model

In order to develop a controller, an accurate drivetrain model is required

to predict the vehicle's response to a torque input. The model can then be used to design and simulate the closed-loop control system.

A piece-wise affine (PWA) three inertias model, which takes into consideration the clutch flexibility together with the driveshaft flexibility, was


48

derived from the laws of motion (Kiencke & Nielsen, 2005; Grotjahn et al., 2006; Van Der Heijden et al., 2007), in which the first inertia corresponds to the engine, the second one includes the inertia of the gearbox and the inertia of the final drive, and the last inertia corresponds to the wheel and vehicle mass, as it is represented in Fig. 4.

The clutch torsional flexibility is a result of an arrangement with smaller stiffness springs in series with springs with higher stiffness. The reason for this arrangement is vibration insulation. Unlike in the above mentioned modeling approaches, where only two or three working modes are considered for the clutch dynamics, in this paper four working modes for the clutch are introduced, i.e., open, closing, closed and locked, which yields a more accurate model of the clutch.

Fig. 4 – Three inertias drivetrain model including the clutch.

In the open mode, there is no mechanical connection between the engine and the rest of the driveline, so no torque is transmitted towards the wheels. In the closing mode, the smaller springs in the clutch are compressed, which means that the engine torque is gradually transmitted to the driveline. In the closed mode, the stiffer springs in the clutch are compressed and the gradual transmission of torque to the driveline continues. The locked mode corresponds to the phase when the springs in the clutch cannot be compressed any further, i.e., the clutch hits a mechanical stop, and the maximum amount of torque is transmitted from the engine to the wheels through the driveline.

3.1. Continuous-Time Model

Consider as state variables: the torsional angle between engine and

transmission, the torsional angle between final drive and wheel, the angular speed of the engine, the angular speed of the transmission and the angular speed of the wheel, and as control input of the system, the engine torque, i.e.,


49

1 2 3 4 5, , , , , ,t

e t t w e t w e

f

x i x x x x u Ti

θθ θ θ ω ω ω= − = − = = = = (1)

where eθ ,

tθ and wθ are angles of the engine, transmission and wheel,

respectively, eω ,

tω and wω are the angular speeds of the engine, transmission

and wheel, respectively, eT is the torque produced by the engine and

ti and fi

are the transmission and final drive gear ratios, respectively. The following PWA state-space model is obtained:

[ ]

( ) ( ) ( ) i

( ) 0

f

0 0 0 ( )

( )

w

it t u t t

y t r t=

= + + ∈Ωci c cx A x b

x

f xɺ

(2)

where 51 5: ( , , )x x= … ∈x ℝ

⊤ and [1,4]:i∈ = ℤI . Here i denotes the active mode

at time t +∈ℝ , 5 5×∈ciA ℝ , 5 1×∈cb ℝ are the system matrices and 5 1×∈cf ℝ is the

affine term. Notice that although there are 3 angles, only two states are introduced as only the angle difference is relevant. The collection of sets i iΩ ∈I∣ defines a partition of the state-space 5⊆ ℝX as follows:

5 closing1 3

5 closing2 3 1 1

5 closing3 3 1 1 2

5 closing4 3 2 1

: ,

: | | ,

: | | ,

: | | ,

&

&

&

e

e

e

e

x

x x

x x

x x

ω

ω θ

ω θ θ

ω θ

Ω = ∈ ≤

Ω = ∈ > ≤

Ω = ∈ > < ≤

Ω = ∈ > <

x

x

x

x

ℝ

ℝ

ℝ

ℝ

|

|

|

|

(3)

where closingeω is the engine closing speed and 1θ , 2θ are threshold values for the

torsional angle between the engine and the transmission, which determine the working mode of the clutch. Note that when a transition from the open mode to the closing mode occurs, the following reset condition must be imposed:

1 1 21 2 1 [ , ) 2 2 1 2, , if ( ) , and ( ) , set ( ) : 0t t tt t t x tτ τ+ >∀ ∈ ∀ ∈ ∈Ω ∀ ∈ ∈Ω =x xℝ ℝ ℝ (4)

As the engine angle eθ tends to infinity in the open mode, so the state

1x tends to infinity, synchronization of the engine angle and the transmission angle must be attained at the moment the clutch switches from the open mode to the closing mode. Notice that the reset condition does not apply to initial conditions, as the model cannot be initialized in the closing mode. The matrices

ciA , cb and cf can be obtained by deriving the whole dynamical model of the drivetrain using the generalized Newton's second law of motion as in (Căruntu et al., 2011). The output of the system is the vehicle speed ( ) w wy t r ω= .


50

The engine torque (i.e., the control input) is restricted by lower and upper bounds:

max0 ( ) , ,eu t T t +≤ ≤ ∀ ∈ℝ (5)

where maxeT is the maximum torque that can be generated by the internal

combustion engine.

3.2. Drivetrain Model Including the Delays

In order to apply the predictive control strategy based on input-output models that will be described in the next section, consider the plant described by the CARIMA (Controlled AutoRegressive Integrated Moving Average) model (Camacho & Bordons, 2004)

( ) ( ) ( ) ( )( ) ( )

( )

1

1 1

11d

e k C zA z y k z B z u k

D z

−

− − −

−= − + (6)

where d is the delay introduced by the communication network, i.e., CAN,

( )e k is white noise with zero mean value, ( )1A z− and ( )1B z− are the system

polynomials

( )( )

1 11

1 10 1

1 ... ,

... ,

A

A

B

B

n

n

n

n

A z a z a z

B z b b z b z

−− −

−− −

= + + +

= + + + (7)

where An and

Bn represent the polynomials degrees and ( )1C z− and ( )1D z−

are the disturbances polynomials

( )( )

1

1 1

1,

1 .

C z

D z z

−

− −

=

= − (8)

The equation developed in (Klehmet et al., 2008) it is used to determine the upper bound of the communication delays that appear on CAN in automotive applications

( )

( )1

0

2

/j j

ii

j ld

R l c−

=

+ ⋅≤

−∑ (9)

where 136l = bits denotes the maximum frame length including the 6 bit CS time, 500 kbpsR = is the rate of a high-speed CAN,

ic is the cycle length of

the thi − priority message and j is the priority of the node for which the upper


51

bound is being calculated. A cycle length nc , corresponding to a message of

priority n , represents the period after which the message is repeated. Now, consider that the delay introduced by the communication network

cd is time-varying, but bounded

m c Md d d≤ ≤ (10)

where md is the minimum and

Md is the maximum delay, determined using (9), that appear in the communication network.

The delay to be considered by the predictive strategy is taken equal to the minimum delay that can appear in the communication network:

md d= (11)

and instead of the polynomial B , another polynomial Bɶ , identified in order to model the system including the delays between

md and Md is introduced

(Căruntu et al., 2010)

( )1 10 1 ... B

B

n

nB z b b z b z−− −= + + + ɶ

ɶ

ɶ ɶ ɶɶ , (12)

with:

0 1

0 1

,

...... ,

1

(1) (1).

B

B

B M mB

n

n

B

n n d d

b b bb b b

n

B B

= + −

+ + += = = =

+

=

ɶ

ɶ

ɶ

ɶ ɶ ɶ

ɶ

(13)

4. Networked Predictive Control Strategy

Predictive control techniques have been introduced mainly in order to

deal with plants that have complex dynamics (unstable inverse systems, time-varying delay, etc.) and plant model mismatch. They are of a particular interest from the point of view of both broad applicability and implementation simplicity, being applied on large scale in industry processes, having good performances and being robust at the same time.

The prediction model is given by

( ) ( ) ( ) ( )

( ) ( )( )

( )( )( )

( )

1 1 1

1 1 1

1 1

ˆ |

1

d

j d

j d j

y k j k G z D z z u k j

H z D z F zu k y k

C z C z

− − − −−

− − −−

− −

+ = + +

+ − + (14)


52

with ,j hi hp= , where hi is the minimum prediction horizon and hp is the

prediction horizon. ( 1 ), 1,u k j k j hc+ − = is the future control, computed at

time k and ˆ( )y k j k+ are the predicted values of the output, hc being the

control horizon.

For determining the polynomials ( )1jF z− , ( )1

jG z− and ( )1jH z− the

two well known Diophantine equations (Camacho & Bordons, 2004) are used.

Considering as inputs ( ) ( )1D z u k− and collecting the j-step predictors

in a matrix notation, the prediction model can be written as

ˆ ˆd 0= +y Gu y (15)

where ˆ0y represents the free response and the matrix G is given in (Camacho &

Bordons, 2004). The objective function is based on the minimization of the tracking

error and on the minimization of the controller output, the control weighting factor λ being introduced in order to make a trade-off between these objectives

( ) ( )Tˆ ˆ λ T

d 0 d 0 d dJ = + − + − +Gu y w Gu y w u u (16)

subject to: [ ]1( ) ( ) 0 for , 1D z u k i i hc hp d− + = ∈ − − , where w is the reference

trajectory vector with the components ( ), ,w k j k j hi hp+ = . Minimizing the

objective function ( 0dJ∂ ∂ =u ), the optimal control sequence yields as

( ) [ ]1

ˆT T

d hc 0λ−∗ = + −u G G I G w y (17)

Using the receding horizon principle and considering that [ ], ,j j hi hpγ =

are the elements of the first row of the matrix ( ) 1T T

hcλ−

+G G I G , the following

control algorithm results

( ) ( ) ( ) ( )10ˆ

hp

j

j hi

D z u k w k j k y k j kγ−

=

= + − + ∑ (18)

5. Experimental Results

This section presents the validations of the proposed networked predictive control strategy investigated on the vehicle drivetrain model using the designed real-time simulation test-bench. The simulator's console software (GUI), designed in ControlDesk, is represented in Fig. 5, including the messages sent on CAN.


53

Fig. 5 – ControlDesk console.

Using typical values for the parameters in (9), it yields that the sum of

the delays ( sc caτ τ+ ) is randomly distributed in the interval [ ]0,4 sT , where

5sT = ms is the sampling period of the system. Furthermore, being components of the same powertrain subsystem it can be considered that the communication delays from sensor to controller and from controller to actuator have the same values and they are uniformly distributed.

In order to apply the predictive control strategy, a CARIMA model for the drivetrain system was identified, using as input the engine torque

eT and as

output the vehicle speed vv and the parameters given in (Căruntu et al., 2011).

Using a pseudo-random binary sequence (PRBS) as the system input of the PWA state-space model (2), the drivetrain was identified with an ARX equivalent model employing the design model, which is given by the following system polynomials

( )( )

1 1 2

1 6 4 1

1 1.962 0.96203 ,

0.77 10 0.143 10 .

A z z z

B z z

− − −

− − − −

= − +

= − ⋅ + ⋅ (19)

It was considered that 0md = and 4Md = from (9) and the polynomial

( )1B z−ɶ was determined using (12) and (13) which yielded

( )1 1 2 30.3383 0.3383 0.3383 0.3383B z z z z− − − −= + + +ɶ (20)

The predictive control strategy was designed using the following parameters: 3, 1,A Dhc n n hi d hp hc d= + = = + = + , with d from (11). The control action was then applied to the initial model of the vehicle drivetrain.


54

Also, a PI controller was designed using (O’Dwyer, 2006) and it was tuned to have a fast response, which yielded the proportional and integral terms:

0.01 and 0.001R iK K= = . A sequence of stairs was applied as the reference for the vehicle

velocity and it was desired that the system tracks the reference signal as fast as possible, the following figures showing the results obtained in the simulations.

Fig. 6 illustrates the reference vehicle velocity value and the response of the system with communication delay when both control strategies are applied. It can be seen that the closed-loop control system tracks the reference signal, having no steady state error.

Fig. 6 – Vehicle velocity.

The working modes of the clutch are presented in Fig. 7, where it can be seen that the clutch starts from open mode and after it reaches the engine closing speed closing

eω , the clutch enters in closing mode. In order to put the vehicle in motion, the load torque has to be defeated, so the engine speed varies around the threshold value, which results in the switching between the open and closing mode. Note that when the clutch springs are being compressed, the clutch enters the closed mode.

Fig. 7 – Clutch mode of operation.


55

As it can be seen in Fig. 8 the engine torque (control signal) for both control methods have smooth behaviours so the stiffer springs do not fully compress. As a result, the first state 1x does not pass the 2θ threshold so the clutch cannot reach the looked mode.

Fig. 8 – Engine torque (control signal).

Fig. 9 illustrates the driveshaft torque in which it can be seen that the

switching between the clutch modes results in very high increases/decreases in the driveshaft torque. Note that when the vehicle velocity reference is switched from 30 km/h to 10 km/h, the driveshaft torque reaches negative values corresponding to the engine breaking.

Fig. 9 – Driveshaft torque.

The axle wrap angular speed ( , where d e tot w tot t fi i i iω ω ω= − = ⋅ ) is

represented in Fig. 10 (and a detail in Fig. 11) as a measure of driveline oscillations that appear when the clutch switches through the operating modes.


56

Fig. 10 – Speed difference.

In the figures it can be seen that, due to the undershoot of the PI response (represented in Fig. 6), the clutch opens (Fig. 7), which results in an increased driveshaft torque (Fig. 9) and driveline oscillations (Figs. 10 and 11).

6. Conclusions

This paper considered the design of a real-time simulation test-bench in

order to validate and to test the performances of the control algorithm developed with the aim of damping driveline oscillations, while decreasing the influence of the communication time-varying delays on the closed-loop control performances over the CAN network. The designed predictive strategy, which uses an identification method to model the physical plant (vehicle drivetrain) including the delays, was tested on the real-time simulation test-bench and the experiments designed based on realistic scenarios verify the better performances of the proposed method when comparing the results with the ones obtained with classical controllers (PI).

Fig. 11 – Speed difference - detail.


57

REFERENCES

Berriri M., Chevrel P., Lefebvre D., Active Damping of Automotive Powertrain

Oscillation by a Partial Torque Compensator. Control Engineering Practice, 16, 874−883, 2008.

Camacho E.F., Bordons C., Model Predictive Control. Springer Verlag, 2004. Căruntu C.F., Bălău A.E., Lazăr C., Networked Predictive Control Strategy for an

Electro-Hydraulic Actuated Wet Clutch. Proc. of the 6th IFAC Symposium Advances in Automotive Control, 419–424, 2010.

Căruntu C.F., Bălău A.E., Lazăr M., Van den Bosch P.P.J., Di Cairano S., A Predictive Control Solution for Driveline Oscillations Damping. Hybrid Systems: Computation and Control, Chicago, USA, 181−190, 2011.

Di Cairano S., Yanakiev D., Bemporad A., Kolmanovsky I., Hrovat D., Model

Predictive Powertrain Control: an Application to Idle Speed Regulation. Lecture Notes in Control and Information Sciences, Springer, 2010.

Fredriksson J., Weiefors H., Egardt B., Powertrain Control for Active Damping of

Driveline Oscillations. Vehicle System Dynamics, 37, 359–376, 2002. Grotjahn M., Quernheim L., Zemke S., Modelling and Identification of Car Driveline

Dynamics for Anti-Jerk Controller Design. Proc. of IEEE International Conference on Mechatronics, Budapest, Hungary, 131–136, 2006.

Kiencke U., Nielsen L., Automotive Control Systems: for Engine, Driveline, and

Vehicle. Springer Verlag, 2005. Klehmet U., Herpel T., Hielscher K.-S., German R., Delay Bounds for CAN

Communication in Automotive Applications. Proc. 14th GI/ITG Conference Measurement, Modelling and Evaluation of Computer and Communication Systems, Dortmund, Germany, 2008.

Lefebvre D., Chevrel P., Richard S., An H-Infinity-Based Control Design Methodology

Dedicated to the Active Control of Vehicle Longitudinal Oscillations. IEEE Transactions on Control System Technology, 11, 2003.

O’Dwyer A., Handbook of PI and PID Controller Tuning Rules. Imperial College Press, 2, 2006.

Pettersson M., Driveline Modeling and Control. Ph. D. Dissertation, Linkoping University, 1997.

Richard S., Chevrel P., Maillard B., Active Control of Future Vehicle Drivelines. In Proc. of the 38th IEEE Conference on Decision and Control, Phoenix, Arizona, USA, 3752–3757, 1999.

Rostalski P., Besselmann T., Maric M., Van Belsen F., Morari M., A Hybrid Approach

to Modelling, Control and State Estimation of Mechanical Systems with

Backlash. International Journal of Control, 80, 1729−1740, 2007. Stewart P., Zavala J., Flemming P., Automotive Drive by Wire Controller Design by

Multi-Objectives Techniques. Control Engineering Practice, 13, 257−264, 2005.

Van Der Heijden A., Serrarens F., Camlibel M., Nijmeijer H., Hybrid Optimal Control

of Dry Clutch Engagement. International Journal of Control, 80, 1717–1728, 2007.


58

SIMULAREA ÎN TIMP REAL A UNUI LANł DE TRANSMISIE

A PUTERII CONTROLAT PRIN REłEA UTILIZÂND REGLAREA PREDICTIVĂ

(Rezumat)

Această lucrare propune o implementare în timp real a unui regulator predictiv

proiectat pentru a amortiza oscilaŃiile care apar în lanŃul de transmisie a puterii la autovehicule, ceea ce este esenŃial în îmbunătăŃirea manevrabilităŃii şi confortului pasagerilor. De asemenea, regulatorul permite compensarea întârzierilor variabile în timp care apar ca urmare a transmiterii mărimilor de comandă şi a mărimilor măsurate de senzori prin reŃeaua de comunicaŃii Controller Area Network (CAN). Metodologia are la bază mai mulŃi paşi: în primul rând, este descrisă structura de timp real cu reŃea CAN integrată şi apoi este prezentat modelul lanŃului de transmisie a puterii la autovehicule. Apoi, Ńinând cont de faptul că întârzierile variabile în timp introduse de reŃeaua CAN sunt mărginite, este propusă o metodă de a modela procesul fizic (lanŃul de transmisie a puterii) care să includă şi întârzierile. În final, este proiectată o strategie de control predictiv care utilizează modelul dezvoltat anterior şi care este concepută pentru a amortiza oscilaŃiile lanŃului de transmisie a puterii. Sistemul de control este testat utilizând structura de timp real şi experimentele bazate pe scenarii realiste ilustrează faptul că regulatorul propus poate avea performanŃe mai bune faŃă de regulatoarele clasice (PI).

real-time simulation of a network … · are read from a database container (dbc) file. ......

Documents