Neuro-Hybrid Models for Automotive System Identification
In automotive systems almost all steps concerning the
calibration of several control systems, e.g., low idle governor or
boost pressure governor, are made with the vehicle because the timeto-
production and cost requirements on the projects do not allow for
the vehicle analysis necessary to build reliable models. Here is
presented a procedure using parametric and NN (neural network)
models that enables the generation of vehicle system models based
on normal ECU engine control unit) vehicle measurements. These
models are locally valid and permit pre and follow-up calibrations so
that, only the final calibrations have to be done with the vehicle.
[1] Kraftfahrtechnisches Taschenbuch, 25th ed., Robert Bosch GmbH,
Plochingen, Germany, 2003.
[2] L. Guzzella, and A. Amstutz, "Control of diesel engines," IEEE Control
Systems Magazine, vol. 18, no. 5, pp. 53-71, October 1998.
[3] Y.-W. Kim, G. Rizzoni, and V. Utkin, "Automotive engine diagnosis
and control via nonlinear estimation," IEEE Control Systems Magazine,
vol. 18, no. 5, pp. 84-99, October 1998.
[4] L. Ljung, System Identification: Theory for the User, 2nd Ed. Prentice
Hall, New Jersey USA, 1999.
[5] K. J. Astrom, and B. Wittenmark, Adaptive Control, 2nd Ed. Addison
Wesley, CA USA, 1995.
[6] S. Sastry, Nonlinear Systems: Analysis, Stability and Control. Springer,
New York USA, 1999.
[7] N. K. Bose, and Ping Liang, Neural Network Fundamentals with
Graphs, Algorithms, and Applications. McGraw-Hill International
Editions, Singapure, 1996.
[8] J. A. Borrie, Modern Control Systems: A Manual of Design Methods.
Prentice Hall, Hertfordshire UK, 1986.
[9] G. F. Franklin, J. D. Powell, and M. Workman, Digital Control of
Dynamic Systems, 3rd Ed. Paddison Wesley, CA USA, 1998.
[1] Kraftfahrtechnisches Taschenbuch, 25th ed., Robert Bosch GmbH,
Plochingen, Germany, 2003.
[2] L. Guzzella, and A. Amstutz, "Control of diesel engines," IEEE Control
Systems Magazine, vol. 18, no. 5, pp. 53-71, October 1998.
[3] Y.-W. Kim, G. Rizzoni, and V. Utkin, "Automotive engine diagnosis
and control via nonlinear estimation," IEEE Control Systems Magazine,
vol. 18, no. 5, pp. 84-99, October 1998.
[4] L. Ljung, System Identification: Theory for the User, 2nd Ed. Prentice
Hall, New Jersey USA, 1999.
[5] K. J. Astrom, and B. Wittenmark, Adaptive Control, 2nd Ed. Addison
Wesley, CA USA, 1995.
[6] S. Sastry, Nonlinear Systems: Analysis, Stability and Control. Springer,
New York USA, 1999.
[7] N. K. Bose, and Ping Liang, Neural Network Fundamentals with
Graphs, Algorithms, and Applications. McGraw-Hill International
Editions, Singapure, 1996.
[8] J. A. Borrie, Modern Control Systems: A Manual of Design Methods.
Prentice Hall, Hertfordshire UK, 1986.
[9] G. F. Franklin, J. D. Powell, and M. Workman, Digital Control of
Dynamic Systems, 3rd Ed. Paddison Wesley, CA USA, 1998.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:59709", author = "Ventura Assuncao", title = "Neuro-Hybrid Models for Automotive System Identification", abstract = "In automotive systems almost all steps concerning the
calibration of several control systems, e.g., low idle governor or
boost pressure governor, are made with the vehicle because the timeto-
production and cost requirements on the projects do not allow for
the vehicle analysis necessary to build reliable models. Here is
presented a procedure using parametric and NN (neural network)
models that enables the generation of vehicle system models based
on normal ECU engine control unit) vehicle measurements. These
models are locally valid and permit pre and follow-up calibrations so
that, only the final calibrations have to be done with the vehicle.", keywords = "Automotive systems, neuro-hybrid models,demodulator, nonlinear systems, identification, and neural networks.", volume = "1", number = "12", pages = "736-4", }