Neural network modeling and control of an anti-lock brake system

The authors have previously described neural-network-based methods for modeling automotive systems and training near-optimal controllers. These methods are based on the premise that the physical system can be sufficiently instrumented during network training so that accurate evaluation of the effect...

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Bibliographic Details
Published inProceedings of the Intelligent Vehicles `92 Symposium pp. 179 - 184
Main Authors Davis, L.I., Puskorius, G.V., Yuan, F., Feldkamp, L.A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1992
Subjects
Automotive engineering
Control systems
Costs
Design methodology
Instruments
Laboratories
Neural networks
Process design
System testing
Vehicles
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Summary:The authors have previously described neural-network-based methods for modeling automotive systems and training near-optimal controllers. These methods are based on the premise that the physical system can be sufficiently instrumented during network training so that accurate evaluation of the effect of control actions is possible. In certain systems, such a automotive anti-lock braking (ABS), it may be costly to obtain the detailed data that would be required to exploit the full capabilities of neural methods. The present paper reports an initial simulation-based study to determine the performance potential of controllers designed with these methods. Such studies will help determine whether the cost of carrying out neural training methods on actual systems is justified.< >
ISBN:078030747X
9780780307476
DOI:10.1109/IVS.1992.252253