Trends in Automotive
Computer Control Systems
In addition to the computer control of
power trains, other major subsystems on the vehicle are now under computer control. For
example, Ford introduced a production com- puter-controlled suspension system in 1984
and a computer-controlled antilock braking system in 1985. Most control systems to date tend to be input-output (I/O) oriented as op- posed to signal processing oriented. For ex-
ample, the advantage of Ford's EEC-IV power train control computer is mainly due to the way that it deals with the vast amount of input information and output commands
1191, [20]. Another example of this philos-
ophy is the Ford electronically controlled air
suspension (EAS) system described in [20],
The EEC-IV and EAS computer control systems emphasize digital-information-type
sensors (exhaust gas oxygen, rpm, Hall ef-
fect devices), digital output devices (sole- noids, relays), and mode-selection strate- gies. From a control point of view, the characteristics of such first-decade systems
may be summarized as follows:
Feedback Conirol: Low level, because feedback sensing devices are switch ori-
ented; high level of scheduled and feed-
forward control.
Arfuprive Conirol: Low level, table ori-
ented; lack of "rich" feedback informa- tion and process models limits the appli-
cation.
Diugmciics: Concerned mainly with di-
agnosing faults in the control system and
not the process under control; again, lack of rich feedback information and process
models limits the application.
Maintenance-on-Demand: Operation con-
dition calculation oriented; lack of sensors and appropriate feedback information lim-
its the application.
Communications: Relatively low speed
and limited to noncritical operations; high- speed, critical applications will depend on
a higher level of multiplexing and/or highly interactive control of major sub- systems.
There is considerable worldwide research
and development in the areas of automotive dynamic system modeling and sensors. As
these areas mature, the role of control theory
should increase even more in automotive control systems because the control prob- lems will become more feedback and signal
processing oriented (as opposed to the cur-
rent I/O and mode-selection orientation). In fact, four of the major areas mentioned
(feedback, adaptive control, diagnostics, and
maintenance-on-demand) should be contain-
able within the same control/signal process- ing framework. For example, rich feedback
signals will be employed for immediate
(foreground) system control, and signal pro-
cessing will be applied in the background to
the same signals to adjust controller gains (adaptive), determine system faults (diag- nostics), and determine the state of the sys- tem with respect to maintenance require- ments (maintenance-on-demand).
Trends in Automotive Computer Control Systems In addition to the computer control of power trains, other major subsystems on the vehicle are now under computer control. For example, Ford introduced a production com- puter-controlled suspension system in 1984 and a computer-controlled antilock braking system in 1985. Most control systems to date tend to be input-output (I/O) oriented as op- posed to signal processing oriented. For ex- ample, the advantage of Ford's EEC-IV power train control computer is mainly due to the way that it deals with the vast amount of input information and output commands 1191, [20]. Another example of this philos- ophy is the Ford electronically controlled air suspension (EAS) system described in [20], The EEC-IV and EAS computer control systems emphasize digital-information-type sensors (exhaust gas oxygen, rpm, Hall ef- fect devices), digital output devices (sole- noids, relays), and mode-selection strate- gies. From a control point of view, the characteristics of such first-decade systems may be summarized as follows: Feedback Conirol: Low level, because feedback sensing devices are switch ori- ented; high level of scheduled and feed- forward control. Arfuprive Conirol: Low level, table ori- ented; lack of "rich" feedback informa- tion and process models limits the appli- cation. Diugmciics: Concerned mainly with di- agnosing faults in the control system and not the process under control; again, lack of rich feedback information and process models limits the application. Maintenance-on-Demand: Operation con- dition calculation oriented; lack of sensors and appropriate feedback information lim- its the application. Communications: Relatively low speed and limited to noncritical operations; high- speed, critical applications will depend on a higher level of multiplexing and/or highly interactive control of major sub- systems. There is considerable worldwide research and development in the areas of automotive dynamic system modeling and sensors. As these areas mature, the role of control theory should increase even more in automotive control systems because the control prob- lems will become more feedback and signal processing oriented (as opposed to the cur- rent I/O and mode-selection orientation). In fact, four of the major areas mentioned (feedback, adaptive control, diagnostics, and maintenance-on-demand) should be contain- able within the same control/signal process- ing framework. For example, rich feedback signals will be employed for immediate (foreground) system control, and signal pro- cessing will be applied in the background to the same signals to adjust controller gains (adaptive), determine system faults (diag- nostics), and determine the state of the sys- tem with respect to maintenance require- ments (maintenance-on-demand).
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