Design of Computer Controlled

7/27/2019 Design of Computer Controlled

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Design of computer controlledcombustion engines

Rolf Isermann *, Norbert Muuller

Laboratory of Control Systems and Process Automation, Institute of Automatic Control,

Darmstadt University of Technology, Landgraf-Georg-Str. 4, D-64283 Darmstadt, Germany

Abstract

Globalization and growing new markets, as well as increasing emission and fuel con-

sumption requirements, force the car manufacturers and their suppliers to develop new engine

control strategies in shorter time periods. This can mainly be reached by development tools

and an integrated hardware and software environment enabling rapid implementation and

testing of advanced engine control algorithms.

The structure of a rapid control prototyping (RCP) system is explained, which allows fast

measurement signal evaluation, and rapid prototyping of advanced engine control algorithms.

A hardware-in-the-loop simulator for diesel engine control design is illustrated, simulation

results for a 40 tons truck are presented. Providing efficient engine models for the proposed

development tools, a dynamic local linear neural network approach is explained and applied

for modelling the NOx emission characteristics of a 1.9 l direct injection diesel engine. Fur-

thermore the application of a RCP system is exemplified by the application of combustion

pressure based closed-loop ignition timing control for a SI engine. Experimental results are

shown for a 1.0 l SI engine on a dynamic engine test stand.

2003 Elsevier Ltd. All rights reserved.

Keywords: Computer-aided control system design; Computer-aided testing; Engine control; Identification;

Engine modelling; Learning systems; Feedforward control

1. Introduction

The last two decades in the automotive industry have seen an ever-increasing

usage of electronics. In the middle 1970s car manufacturers introduced micropro-

cessor-based engine control systems to meet state regulations and customer demands

Mechatronics 13 (2003) 10671089

*

Corresponding author. Tel.: +49-6151-162114; fax: +49-6151-293445.E-mail addresses: [email protected] (R. Isermann), [email protected]

(N. Muuller).

0957-4158/$ - see front matter 2003 Elsevier Ltd. All rights reserved.

doi:10.1016/S0957-4158(03)00043-6
http://mail%20to:%[email protected]/http://mail%20to:%[email protected]/


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of high fuel economy, low emissions, best possible engine performance and ride

comfort. Especially the American regulations (clean air act 1963, on-board diagnosis

(OBD I 1988, OBD II 1994)) and the corresponding European regulations EURO 1

(1992), EURO 2 (1996), EURO 3 (2000) had a considerable effect on the develop-ment of new engine control methods. New sensors with electrical output and new

actuators had to be developed. Also auxiliary units like electro-mechanical con-

trolled carburetors, low pressure injection systems for SI engines, and high pressure

injection systems for diesel engines have shown a development from pure mechanical

to electro-mechanical devices with electronic control. New actuators were added like

for exhaust gas recirculation (EGR), camshaft positioning and variable geometry

turbochargers (VTG). Todays combustion engines are completely microcomputer

controlled with many actuators (e.g. electrical, electro-mechanical, electro-hydraulic

or electro-pneumatic actuators, influencing spark timing, fuel-injector pulse widths,

exhaust gas recirculation valves), several measured output variables (e.g. pressures,temperatures, engine rotational speed, air mass flow, camshaft position, oxygen-

concentration of the exhaust gas), taking into account different operating phases (e.g.

start-up, warming-up, idling, normal operation, overrun, shut down). The micro-

processor-based control has grown up to a rather complicated control unit with 50

120 look-up tables, relating about 15 measured inputs and about 30 manipulated

variables as outputs. Because many output variables like torque and emission con-

centrations are mostly not available as measurements (too costly or short life time) a

majority of control functions is feedforward.

In the future increasing computational capabilities using floating point proces-

sors will allow advanced estimation techniques for non-measurable quantities likeengine torque or exhaust gas properties and precise feedforward and feedback

control over large ranges and with small tolerances. New electronically controlled

actuators and new sensors entail additional control functions for new engine

technologies (VTG turbo chargers, swirl control, dynamic manifold pressure,

variable valve timing (VVT) of inlet valves, combustion pressure based engine

control).

The following chapter gives an overview on engine control structures of state-of-

the-art SI and diesel engines. Modern development and testing tools applied for

engine control algorithms are outlined in Section 3. Sections 4 and 5 explain in more

detail some basics and the structure of rapid control prototyping (RCP) and hard-

ware-in-the-loop simulation, respectively. Section 6 is devoted to modelling tech-

niques using local linear neural networks, since efficient process modelling is a

fundamental prerequisite for all of the proposed development tools. The last chapter

explains a combustion pressure based ignition control system as a sophisticated

application example for a RCP system.

2. Control structure for internal combustion engines: state-of-the-art

Modern IC engines increasingly involve more actuating elements. SI engines

haveexcept the classical inputs like amount of injection minj, ignition angle hign,

1068 R. Isermann, N. Muuller / Mechatronics 13 (2003) 10671089


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injection angle hinjadditionally controlled air/fuel ratio, EGR and VVT, see Fig. 1.

The location of sensors and actuators are shown in Fig. 2.

Diesel engines were until 1987 only manipulated by injection mass and injection

angle. Now they have additional manipulated inputs like EGR, variable geometryturbochargers, common rail pressure and modulated injection, see Fig. 3.

Fig. 1. Simplified control structure of a SI engine.

Fig. 2. Location ofsensors and actuators of a SI engine (all of them except cylinder pressure sensors are

state-of-the-art in current engine control units).

R. Isermann, N. Muuller / Mechatronics 13 (2003) 10671089 1069


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The engine control system has therefore to be designed for 510 main manipu-

lated variables and 58 main output variables, leading to a complex nonlinear

multiple-input multiple-output system. Because some of the design requirements

contradict each other suitable compromises have to be made. Since the majority of

control functions are realized by feedforward structures, precise models are required.

Feedback control is used in the case of SI engines for example for the k-control(keeping a stoichiometric relative air/fuel ratio k 1 for best conversion efficiency ofthe catalytic converter), for the electronic throttle control and for the ignition angle

in case of knock. Diesel engines with turbochargers possess a charging pressure

control with waste gate or variable vanes and speed overrun break away. Both

engines have idling speed control and coolant water temperature control. Addi-

tionally, there are several auxiliary closed-loop controls like fuel pressure control and

oil pressure control. All control functions have to be defined and tested for all

possible operating phases.

Most feedforward control functions are implemented as grid-based two-dimen-

sional (i.e. two input signals) look-up tables or as one-dimensional characteristics.

This is because of the strongly nonlinear static and dynamic behavior of the IC

engines and the direct programming and fast processing in microprocessors with

fixed point arithmetics. Some of the functions are based on physical models with

correction factors, but many control functions can only be obtained by systematic

experiments on dynamometers and with vehicles.

3. Development tools for engine control systems

Without appropriate tools for the design, implementation, and testing of new

engine control algorithms, the tight schedules in the development process of engine

Fig. 3. Simplified control structure of a diesel engine with turbocharger.



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Fig. 4. Design and simulation steps for ECU function development of internal combustion engines [14].

Fig. 5. Simulation methods for the development of ECU functions.



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control systems cannot be met anymore. Especially in the automotive industry, the

concurrent design of engine, body and electronics requires efficient development

methods. In this context the simulation increasingly plays an important role in all

steps of the development process, [5]. Fig. 4 shows the employment of simulationtechniques for the design and testing of engine control unit (ECU) functions. As in

many cases basic ECU functions are already existing, this case is considered. De-

pending on the application and the development stage, the following simulation

methods can be used, Fig. 5:

Software-in-the-loop simulation (SiL): In the first step of the function develop-

ment some basic analysis has to be done to develop the control structure and

actuator configuration. A sufficient method is the software-in-the-loop simula-

tion, where a software version of the control function is tested with the simulated

process.

Fig. 6. Simulation system family for software-in-the-loop, control prototyping and HiL.



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Rapid control prototyping (RCP): The control prototyping is used to test the

new control function together with the real already existing control unit and

the simulated or the real process. In contrast to the software-in-the-loop simula-

tion, only a selected subset of the ECU functions is realized as a special softwareimplementation. Only these interesting parts are calculated on a real-time com-

puter system in a bypass mode to the real ECU, Fig. 5.

Hardware-in-the-loop simulation (HiL): After the target code generation of the

control functions, the hardware-in-the-loop simulation is employed for testing

the implemented ECU functionality. In this configuration the real ECU hard-

ware operates together with the simulated process in real-time [16]. To couple

the real-time computer system with the engine control unit, it is necessary to gen-

erate the required sensor signals (e.g. pulses of the crankshaft and camshaft in-

ductive speed sensors, temperatures and pressures) and to process the actuator

signals [12].

Fig. 6 shows a simulation family system for all three simulation categories, SiL, RCP

and HiL [15].

4. Rapid control prototyping and experiment control

In order to allow development and testing of new control functions on-line in

conjunction with the real engine in a comfortable manner, powerful real-time com-

puter systems are required. RCP systems allow the implementation and testing ofnew algorithms together with the already existing ECU. Thus programming and

modifications of the production ECU are avoided.

The structure of a RCP system is explained in the following, it is capable for fast

measurement signal evaluation and advanced engine control algorithms, as it is re-

quired for instance in case of combustion pressure based engine control. It consists

of two subsystems, the RCP and an indication system, see Fig. 7. Both systems

operate in parallel to the production cars ECU, and are based on PowerPCs, type

Motorola MPC 750, 480 MHz, which are designed for real-time use and are pro-

grammed in high level language.

The indicating system allows to evaluate cylinder pressure signals in real-time at a

resolution of 1 crankshaft angle (CA) for four cylinders. Thus it operates in a crank

synchronous manner. Thermodynamic and signal-based cylinder pressure features,

like mean indicated pressure, crank angle of the center of combustion, and location

of peak pressure, are calculated by the indicating system and are transmitted to the

RCP system.

The RCP system of the company dSPACE allows a very fast and easy imple-

mentation and testing of new control concepts on real-time hardware. It computes

the control and optimization algorithms and operates in a time-synchronous man-

ner, at a sampling rate of 1 kHz. The user is enabled to code newly developed al-gorithms from block diagrams (e.g. MATLAB/Simulink) and download the code by

means of an automatic code generation software to the real-time hardware with a



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mouse click. A complete design iteration can thus be accomplished within a few

minutes [5].

The RCP system uses the production car sensors, additional test stand sensors

and the output signals and messages of the ECU. By evaluating the production car

sensor information, the standard-ECU control settings can be investigated and then

be modified, also independent own control strategies can be implemented. The ac-

tuator signals, which are calculated in real-time, are then sent to the actuators or the

ECU by means of a CAN bus or by PWM-signals.

The described real-time hardware system enables very fast and easy implemen-

tation and testing of complex algorithms including extensive data preprocessing for

Fig. 8. Asynchronous motor coupled to the combustion engine.

ElectronicControlUnit

ISA Bus Interface

Host PC

MATLAB/Simulink

Stateflow

ControlDesk

indicatingsystem

PHS

Bus

ISA

Bus

RAMDS4110

MUX-A/DDS2003

TimerDS4201

CANDS4302

PowerPCDS1005

CAN

K-line

dSPACEPX20 Box

RCP-System

PHS

Bus

ISA

Bus

DIO/PWMDS4001

Multi-I/ODS2201-1

ECU Interface

DS4120

Multi-I/ODS2201-2

CANDS4302

PowerPCDS1005

Cylinderpressuresignals

Crankshaftsignal

ISA BUS Interface

Real-TimeInterface

Real-TimeWorkshop

Fig. 7. Rapid control prototyping system configuration.



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the cylinder pressure, even under the hard real-time conditions of combustion en-

gines (see Fig. 8).

5. Hardware-in-the-loop simulation for truck diesel engines

After the evaluation of new control functions by using RCP systems, the program

code is implemented on the real ECU hardware and tested with the real ECU in real-

time, see Fig. 3. Fig. 9 shows the setup of a HiL simulation test bench for testing new

control functions of the real engine control unit of a truck diesel engines together with

the simulated engine. It may be subdivided in the following parts [10]:

real-time computer system including I/O-modules,

periphery, consisting of the sensor and actuator interface,

engine-CAN

control panal Windows-user interface

sensor-signals(p,T, ...)

crankshaft-/camshaft-

signal

control-signals

(e.g.ignition)

speedometer-signal

actuator-control(starter)

actuator-control

(e.g. engine brake)

PLD-control unit(real part)

FMR-control unit(real part) accelerator

pedal(real part)

brake pedal,accelerator pedal,

clutchactuator-interface sensor-interface

magnetic valvecurrent

digital I/O digital I/O

D/A-conversion

IES-CAN

injection pumps(real parts)

IV IV

real-time computersystem

simulation

IES

valve-currentmeasurement

valve-currentanalysis

actuator-box

signal-conditioning

sensorsignal-generation

sensorfault-generation

signal-conditioning

Fig. 9. HiL test bench with simulated engine and vehicle and real-time engine and vehicle, with real engine

control unit and real injection actuators. FMR: vehicle management system; PLD: pump line nozzle.



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PLD control unit including real actuator components, stand-alone or in combina-

tion with the real FMR,

PC with graphical user interface,

control panel.

5.1. The HiL-simulator

The real-time computer system for this simulator is based on a dSPACE-system

equipped with digital signal processors and a DEC Alpha processor. This system has

the advantage of high computing power which makes a parallel calculation unnec-

essary. It offers also the possibility to realize all the models in MATLAB/Simulink to

use all the benefits of a graphical simulation environment. Special I/O-modules

(digital-I/O-module, D/A converter, A/D converter and CAN-interface) are used for

the communication with the periphery. The coupling of the simulator and thecontrol unit is implemented with special periphery which can be subdivided into a

sensor and an actuator interface.

The sensor interface generates the necessary sensor signals like temperatures and

pressures. The pulses of the camshaft and crankshaft inductive speed sensors are

generated with a board specifically designed for high speed signal generation. For

that purpose a lookup-table with the pulse-signals versus the crankshaft angle is

stored off-line in memory. During the simulation, the signals are periodically read

out, synchronous to the simulated engine speed. This realization guarantees a high

flexibility in forming the pulses and adapting different gear wheels. The sensor in-

terface also contains a relay electronic to simulate sensor faults like interruptions andshort circuits.

The actuator interface mainly consists of the injection pumps (pump-line-nozzle

injection system) which are integrated in the simulation test bench as real compo-

nents, because the combination of the PLD control unit and the injection pumps

represent a mechatronic unit which is difficult to model. A special electronic device

measures the magnetic valve currents to reconstruct the real valve opening time and

to determine the pulse width and the beginning of injection. These quantities are

transferred to the real-time computer system for engine simulation. By this way the

real behavior of the injection pumps is included.

The simulator test bench was set up with the objective of testing the PLD control

unit stand alone or in combination with the FMR control unit. In the first case, the

necessary FMR functions are simulated by the computer system. The data transfer is

done via the engine CAN-bus. In the second case the PLD and the FMR control

units are connected directly via the engine CAN-bus. The computer system emulates

other IES units in this operation mode by transmitting the data via the IES-CAN-

bus to the FMR. For an efficient use of the simulator, a comfortable experimental

environment is needed.

The simulator operation is performed with a windows user interface on the host-

PC which copies the functionality of a real truck-cockpit. All relevant simulationquantities can be visualized on-line or be recorded for off-line analysis. To ensure

reproducible results a driver simulation is implemented which can automatically



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follow a given speed cycle by operating the accelerate pedal, brake, clutch and

gear. As alternative, an interactive driving of the simulator can be performed

manually with a control panel where the most important cockpit functionalities are

realized.

5.2. Simulation results

Three HiL simulation examples for an 8-cylinder truck engine (420 kW) are

represented in order to document the applicability and the performance of the sim-

ulator. Fig. 10 demonstrates the effect of switching off a single injection pump valve.

The gearbox is in neutral position and at the beginning the engine runs with idle

speed. The cyclic decrease of the engine torque and the engine speed after the fuel

shut off can directly be seen. The control unit gradually compensates for the missingtorque of one cylinder by increasing the pulse width in order to keep the desired idle

speed.

A full power acceleration of a 40 tons truck including two gear shifts (1) is de-

picted in Fig. 11. The following effects can be observed:

oscillations in the drive chain (2)

maximum speed limit regulation (4)

limitation of soot (3)

lagged reaction of the turbo charging pressure (5)

5 10 15 20 25 30

time [s]

500

-500

0

1000

1500

2000

2500

60

55

65

M

[Nm]

eng

v

[km/h]

veh

0

5

-5

R[%]

vehicle speed

engine torque

road slope0%

-2%

4%

desired value

real value

Fig. 12. HiL simulation, evaluating the cruise control function of a 40 ton truck.



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Fig. 12 shows the simulation results evaluating the cruise control function. The 40

tons truck runs at a constant speed of tveh 60 km/h. After a road slope of aR 2% the engine control unit reduces engine torque Meng until all injection pumps are

switched off (at 10 s). After the change of the road slope to 4% engine torque isincreased in order to compensate for the deviation between desired vehicle speed andsimulated vehicle speed.

The various simulation results illustrate the performance of the HiL simulator. It

allows repeatable testing of engine control units and control algorithms under var-

ious conditions. Thus expensive and probably dangerous engine experiments and

driving maneuvers are avoided. The behavior of the ECU during sensor or actuator

faults can be easily studied.

Several of theses HiL systems were developed for DaimlerChrysler AG, Stuttgart,

Germany.

6. Nonlinear identification of exhaust gas components

Mathematical models of engines are of basic importance, not only for HiL sim-

ulators as shown in the preceding section, but also for feedforward and feedback

control design of engine control units. The approach for modelling the NOx emis-

sions of a 1.9 l direct injection diesel engine is explained in the following.

In order to minimize computational requirements, mathematical models have to

describe the behavior of a system as compactly as possible. Based on physical re-lations, theoretical modellingcan be applied to simulate e.g. the mechanics of pistons

and the crankshaft, leading to so called white-box models. However, in case of

thermodynamic or chemical processes, extravagant requirements in calculating

power rule out physical modelling for control design or real-time usage. Theoretical

modelling of exhaust gas concentrations for instance depends on complex thermo-

dynamic and chemical equations, as well as on boundary conditions like swirl,

tumble, quenching, and local temperatures [7].

This motivates the application of black-box or experimental models, which

model the inputoutput behavior of a system using universal approximators, capable

for multi-dimensional modelling. In contrast to look-up tables, which are only suit-

able for one- or two-dimensional problems, neural networks [11] have been success-

fully applied for high order problems, allowing to consider all relevant influencing

variables. Especially neural networks based on the local linear model approach

(LOLIMOT) showed to be highly efficient and applicable to engine modelling [13].

Depending on the complexity of the engine, more than 57 inputs might be necessary

to consider the most relevant variables concerning the exhaust gas formation. Among

these are e.g. the engine speed, load, injection angle and -pressure, EGR and the

position of the guide blades of turbochargers with VTG. The application of dynamic

neural nets allows to model the dynamic behavior of exhaust gas emissions. Fur-thermore static emission maps can be calculated by means of the dynamic neural

network [4].



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In the following section the structure and training procedure of LOLIMOT, a

local linear model approach, is explained more detailed.

6.1. Fast local linear neural networks

Local linear model tree (LOLIMOT) is an extended radial basis function network

[13], which is extended by replacing the output layer weights with a linear function of

the network inputs. Thus, each neuron represents a local linear model with its cor-

responding validity function. Furthermore, the radial basis function network is

normalized, that is the sum of all validity functions for a specific input combination

sums up to one. The Gaussian validity functions determine the regions of the input

space where each neuron is active. The input space of the net is divided into M

hyper-rectangles, each represented by a linear function.

The output ^yyof a LOLIMOT network with n, x1; . . . ;xn is calculated by summingup the contributions of all M local linear models

^yyXMi1

wi0 wi1x1 winxn Uix 1

where wij are the parameters of the ith linear regression model and xi is the model

input. The validity functions Ui are typically chosen as normalized Gaussian

weighting functions.

Uix liPMj1 lj 2

with

li exp

1

2

x1 ci12

r2i1

1

2

xn cin2

r2in

!with center coordinates cij and dimension individual standard deviations rij. LO-

LIMOT is trained as follows. In an outer loop the network structure is optimized. It

is determined by the number of neurons and the partitioning of the input space,

which is defined by the centers and standard deviations of the validity functions. An

inner loop estimates the parameters and possibly the structure of the local linear

models. The network structure is optimized by a tree construction algorithm that

determines the centers and standard deviations of the validity functions, see Fig. 13.

The LOLIMOT algorithm partitions the input space in hyper-rectangles. In the

center of each hyper-rectangle, the validity function of the corresponding linear

model is placed. The standard deviations are chosen proportional to the size of the

hyper-rectangle. This makes the size of the validity region of a local linear model

proportional to its hyper-rectangle extension. Thus, a model may be valid over a

wide operating range of one input variable but only in a small area of another one.

At each iteration of the outer loop, one additional neuron is placed in a new hyper-rectangle, which is derived from the local linear model with the worst local error

measure



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Jlocal XNj1

Uixj yj ^yyj2 3

In other words, the local error is the sum of the squared errors weighted with the

corresponding validity function Ui over all data samples N. The new hyper-rectangle

is then chosen by testing the possible cuts in all dimensions and taking the one withthe highest performance improvement.

In an inner loop the parameters of the local linear models are estimated by a local

weighted least-squares technique. The prediction errors are weighted with the cor-

responding validity function. Each local linear model is estimated separately, that is

the overlap between neighbored local models is neglected. This approach is very fast

and robust. It is especially important to note that due to the local estimation ap-

proach the computational demand increases only linearly with the number of local

models.

In addition to approximating stationary relations of nonlinear processes, LOLI-

MOT is also capable of simulating the dynamic behavior of processes. In order tomodel multivariate, nonlinear, dynamic processes, the following time-delay neural

network approach can be taken.

^yyt fxt

xt u1t 1; . . . ; u1t m; . . . ; upt 1; . . . ; upt m; ^yyt 1; . . . ; ^yyt mT

4

where m is the dynamic order of the model, u1t; . . . ; upt are the pmodel inputs and^yyt is the model output at time t, see Fig. 14.Some of the main advantages of the LOLIMOT approach is its fast training timeof some 1030 s, compared to many minutes to hours with other neural networks, its

Fig. 13. First four iterations of LOLIMOT training algorithm.



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applicability to adaptive problems [3] and the interpretability of the net structure andparameters in a physical sense.

6.2. Modelling the NOx-emissions of a diesel engine

Since experimental modelling is based on measurement data, engine experiments

are essential. When recording the training data for dynamic models, one should keep

in mind, that the measured data has to contain the whole range of amplitudes and

dynamics of the process in order to get satisfying results. APRBS signals (amplitude

modulated pseudo random binary signal) are often suitable as process inputs becausethey excite the process within a wide range of amplitudes and frequencies. In this

example, step responses of differing amplitudes have proven to be suitable for the

training of the NOx-models.

Fig. 15 shows for the training data set the dynamically measured NOx emissions

and the three input signals, fuel mass minj, engine speed neng and CA of injection hinj(EGR and VTG were disabled during this test).

The neural network approach based on LOLIMOT networks consists of dynamic

local linear models, each of them being valid in a specific operating domain. The

structure of each local linear model was selected as

NbOOXk fLOLIMOTminjk 1; nengk 1; hinjk 1;NbOOXk 1 5

Fig. 14. LOLIMOT net with external dynamics. LLM: local linear models. The filters are chosen as simple

time delays q1.



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giving a simple linear model of first order. After building the neural model, the

model can be applied to a new (unknown) data set and simulates the NO x according

to the respective input data (on-line in the vehicle, if necessary).

Fig. 16 illustrates the good generalization performance of a first order dynamic

net with 15 neurons for the NOx emissions. Despite the excitation of the input signals

in Figs. 15 and 16 were of quite different quality, the simulated NOx is able to followthe measured values with an error of just a few percent, which was quite satisfying.

Another important feature of these dynamic neural models is the possibility of

deriving stationary maps from dynamic models. The data in Fig. 15 led to a dynamic

model according to (5). This model was then used to calculate the stationary

Training

neng

[rpm]

minj

[mg]

inj

[CA]

NO

X

[ppm]

time [s]

0

1000

2000

4000

0

0

50

-20

0

0

0

100

100

200

200

300

300

400

400

500

500

600

600

Fig. 15. Training data set for a dynamic NOx model.

Generalization

neng

[rpm]

minj

[mg]

inj

[CA]

NO

X

[ppm]

time [s]

0

1000

2000

5000

0

0

50

-20

0

0

0

50

50

100

100

150

150

200

200

250

250

300

300

350

350

Fig. 16. Generalisation of the NOx model trained with the data in Fig. 15.



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two-dimensional look-up table shown in Fig. 17. Thus, it is possible to get a good

visual overview on the engines characteristics by maps derived from dynamic

measurements. Even more importantly, this feature allows a fast dynamic mea-

surement of the engines characteristics and a calculation of the static engine maps by

means of the dynamic neural model. Consequently, there is no need to measure the

whole look-up table using measurement points on equidistant grids, which helps to

save expensive measurement time.The obtained dynamic model for the NOx emissions can be used for off-line or on-

line optimization of exhaust gas emissions, and has been implemented on a RCP

system as explained in chapter. In the same way nonlinear models are obtained for

torque, fuel-consumption, CO, HC and soot [4].

7. Adaptive feedforward control of ignition angle with rapid control prototyping for an

SI engine

Cylinder pressure signals contain valuable information for closed-loop engine

control. For using this information low cost combustion pressure sensors with high

long-term stability have been developed [1,6] and are starting to be installed into

production engines [8]. Real-time cylinder pressure evaluation is, however, a de-

manding task and requires powerful computational resources. Sampling of cylinder

pressure signals is usually performed in a crankshaft synchronous manner, a typical

resolution for engine research is 1 crank angle (CA), which results in sampling rates

up to 40 kHz, depending on engine rotational speed. Nevertheless, increasing com-

putational performance as well during the control design stage using RCP systems,

as for future engine control units entail an increasing interest in benefiting from

combustion pressure information.

inj

inj

[CA]m [mg]

NOx [ppm]

Fig. 17. NOx Map, calculated from dynamic neural net (neng 2500 1/min).



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A variety of engine control functions could be improved or implemented using

cylinder pressure sensors. One of them is the implementation of a closed-loop

ignition control system, as it is explained in the following.

The objective of ignition control is to achieve optimum engine efficiency for eachcombustion event. Generally factors that influence the optimal ignition angle are

engine specifications like configuration of the combustion chamber, operating con-

ditions like engine speed, load, temperature and EGR flow rate, as well as ambient

conditions such as air temperature, air pressure and humidity in the atmosphere.

Standard ignition control systems are based on feedforward control and therefore

rely heavily upon calibration of look-up tables. The database values are ini-

tially calibrated from an analysis of a nominal engine under fixed environmental

conditions. However, changing environmental conditions, aging effects, and manu-

facturing tolerances usually change an engines characteristics and lead to a deteri-

orating performance. This motivates the development of closed-loop controlsystems.

Combustion pressure sensors allows to optimize the point of ignition of each

cylinder separately. The variable which is to be controlled is calculated from the

mass fraction burned (MFB) signal, which can be derived from cylinder pressure

evaluations [9]. Measurements and theoretical analysis reveal, that optimal ignition,

i.e. maximum torque from each combustion event, can be obtained if 50% of fuel

mass have been burned until a crank angle of 8 after top dead center (TDC) [2]. This

crank angle is also referred to as the center of combustion. The proposed ap-

proach calculates the crankshaft angle of 50% MFB h50% MFB for each combustion

cycle and controls it to h50%MFB 8 CA after TDC.

7.1. Control structure

Applying closed-loop ignition control using standard controllers (e.g. of PI type)

cannot provide acceptable control performance under fast changing operating

conditions, since dead times and noisy signals prohibit the tuning of controller gains

with high control effort. The dead times are due to the fact, that after ignition of the

airfuel mixture, the combustion cannot be influenced any further. Therefore the

ignition angle can only be computed for the next cycle, based on measurements from

the present engine cycle, and a dead time of one cycle is inherent. Moreover as there

exist significant cycle fluctuations even under steady operating conditions, the cal-

culated cylinder pressure features of several consecutive engine cycles have to be

averaged (e.g. a moving average over 10 cycles is used). However, because the system

error usually differs from one engine operating condition to another, using con-

trollers with small control gains, it takes a certain amount of time after engine

transients, to integrate to a new ignition angle. The stochastic nature of the

combustion events can be seen in Fig. 18. The upper diagram shows the measured

cylinder pressure signals of the compression and the power strokes of 100 consec-

utive cycles of an arbitrary cylinder. The dotted lines represent the reconstructedpolytrope, which is the component of the cylinder pressure which is due to the piston

motion (also called towed or motored pressure). The lower diagram depicts the



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corresponding crank angle locations of 50% MFB, which are to be controlled to 8

CA.

This motivates the use of learning feedforward control (LFFC), whose general

structure is shown in Fig. 19. The linear controller C is used to compensate random

disturbances and to provide a reference signal for the learning feedforward con-

troller. It does not need to have a high performance and can be designed in such a

way that it provides a robust stability. A function approximator works in parallel to

the linear controller, it can be represented by a neural network or, in the simplest

case, by a two-dimensional look-up table. Since the learning function approximatoracts instead of a controllers integral term, the linear controller is preferably a simple

proportional gain. It can also be deactivated for noise suppression.

150 100 50 0 50 100 1500

10

20

30

40

crank angle [CA]

cyl.pre

ssure[bar]

0 10 20 30 40 50 60 70 80 90 100

5

10

15

cycle number

loc.ofx

MFB

=0.5

[CA]

Fig. 18. Upper diagram: measured cylinder pressure signals and reconstructed prototype. Lower diagram:

calculated crank angle locations of 50% MFB (nmot 3000 rpm, 50% load).

Fig. 19. General structure of a LFFC. The controller C is basically used for the adaption of the feed-

forward map.



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For the ignition control system the function approximator is divided into the

conventional, fixed ignition look-up table and into the adaptive offset map, see Fig.

20. The adaptive look-up table is trained on-line using NLMS or RLS training al-

gorithms [9]. The learning process is performed during normal operation of the

engine, without determining special excitation signals. The learning system is just

collecting and processing inputoutput samples at the individual operating points.

The operating condition is determined by the engine load (a normalized value cal-

culated from the intake manifold pressure signal) and the engines rotational speed.The conventional look-up table determines an approximate value of the ignition

angle hi;c and is valid for all cylinders. For each cylinder the location of 50% MFB is

calculated and compared to the reference location of 8 CA. Correction angles hadaptare calculated and memorized by the adaptive offset look-up table of each cylinder,

at the corresponding operating condition. In order to reduce noise in the controller

output signal hi, no linear proportional controller C is used in parallel to the function

approximator, see Fig. 19.

7.2. Measurement results

In Fig. 21 at a constant engine speed of 2500 rpm a certain load change sequence

is repeated for three times, see the third diagram. The first diagram shows the crank

angle locations of 50% MFB for two cylinders, which are to be controlled to 8 CA

after TDC. The second diagram depicts the correction values hadapt for the ignition

angles of the corresponding cylinders. For the first 50 s only the conventional

feedforward control is active. The considerable deviations of the crank angle loca-

tions of 50% MFB from the optimal value for best engine efficiency at 8 CA after

TDC is visible, see the upper diagram. Between 50 and 168 s the adaptive feedfor-

ward controller is active. Since the adaptive inputoutput map of each cylinder hadbeen initialized to zero, it first has to be adapted for both operating conditions. The

control performance for the already adapted feedforward control is shown between

Fig. 20. Structure of the adaptive feedforward ignition control system.



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130 and 160 s. Then it is switched back to the conventional, fixed feedforward

control.

In spite of the considerable, stochastic nature of the combustion events, the

adaptive feedforward control allows to maintain the mean crank angle location of

50% MFB around its optimal value at about 8 CA after TDC.

8. Conclusions

The demands for improved engine emissions, performance and efficiency, as well

as the tight schedules in the development process, require efficient methods and tools

for the design, implementation and calibration of engine control units.

RCP systems allow a fast and easy implementation and testing of new engine

control algorithms. Graphical user interfaces and automatic code generating meth-

odologies allow the computation of control functions, in bypass to the engine control

unit, on a real-time hardware.

Hardware-in-the-loop simulators allow testing of engine control units and control

algorithms under various conditions and in a repeatable manner. Thus expensive and

probably dangerous engine experiments are avoided and valuable time on dyna-

mometers can be saved. The behavior of the engine control unit during sensor faults

can be studied.

Efficient engine models, as required for all proposed development tools, can be

obtained by using dynamic local linear neural networks. The basic structure of thenetwork type LOLIMOT is explained and applied for modelling and online simu-

lations of the dynamic NOx emissions of a direct injection diesel engine.

0 20 40 60 80 100 120 140 160 180

5

10

15

20

50%MFB,1

+2

[]

0 20 40 60 80 100 120 140 160 180

-5

0

5

adapt

[]

0 20 40 60 80 100 120 140 160 180

15

20

25

30

load[%]

time [sec]

Fig. 21. Control performance of the ignition control system during three equivalent load changes. The

adaptive look-up table is initialised with zero, after 50 s the adaptive feedforward control is activated.



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To exemplify the potential of RCP systems, a combustion pressure based ignition

control system was presented. An indicating system allows the online evaluation of

cylinder pressure signals in real-time, in a crank synchronous manner. Learning

feedforward control is implemented on a time synchronous RCP, allowing to opti-mize the ignition angle of each cylinder separately. The proposed control system is

capable to compensate for manufacturing tolerances, fuel quality variations and

long-term effects such as aging or wear of the engine.

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