METHOD AND DEVICE FOR DIAGNOSING A CONTROL SYSTEM USING A DYNAMIC MODEL

• US 20110087400A1
• Filed: 02/23/2009
• Published: 04/14/2011
• Est. Priority Date: 02/29/2008
• Status: Abandoned Application

• Current Assignee: Renault Sas (Renault)
• Original Assignee: Renault Sas (Renault)
• Inventors: Lorimier, Lionel
• Application Number:
  US12/920,225
• Publication Number:
  US 20110087400A1
• Time in Patent Office: Days
• US Class Current: 701/35
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks

Abstract

A system for diagnosing operation of a control system of at least one automobile driving parameter using a dynamic model, which includes a mechanism that stores on a non-volatile memory the input and output data of the system during the operation, adapted to store the data at a sampling frequency lower than the system sampling frequency, and including a dynamic model that can be stimulated by the stored input data to determine the reconstituted output data, and a comparison mechanism that compares the reconstituted output data with the stored output data for consistency diagnosis.

Claims

1-12. -12. (canceled)

13. A method for diagnosing operation of a system for controlling at least one driving parameter of a motor vehicle, using a dynamic model, the diagnosis being made based on system input and output data that have been recorded during operation, the method comprising:

recording system input and output data with a lower sampling frequency than a system sampling frequency;

stimulating the dynamic model with the recorded input data so as to determine reconstituted output data; and

comparing the reconstituted output data with the recorded output data with a view to a consistency diagnosis.

14. The method as claimed in claim 13, further comprising, before the recording, interpolating the data recorded at the system sampling interval.

15. The method as claimed in claim 13, further comprising, before the recording, reconstituting parameters and coefficients for static correction on the basis of recorded input data.

16. The method as claimed in claim 13, in which the comparing includes comparing a discrepancy between the reconstituted data and the recorded data with a threshold value for each datum and emitting an alert information item if the discrepancy is greater than the threshold value.

17. The method as claimed in claim 13, further comprising, before the stimulating the dynamic model, reconstructing an initial state vector on the basis of recorded input and output data.

18. The method as claimed in claim 17, in which the dynamic model uses, for each sampling interval, discretized dynamic equations involving state variables of the model, the reconstructing the initial state vector including inverting a system of equations comprising recorded initial data and dynamic equations corresponding to a minimum number of sampling intervals on the basis of the initial state.

19. The method as claimed in claim 18, in which the driving parameter that forms a subject of the diagnosis is a deflection request for a rear wheel of a vehicle comprising at least three steerable wheels, the recorded initial data used in the system of equations comprising longitudinal speed of the vehicle, angle of deflection of the front wheels, dynamic part of the rear wheel deflection angle, static part of the rear wheel deflection angle, control value of the rear wheel deflection angle, and the dynamic equations comprise, as variables, modeled value of the rear wheel deflection angle, yaw rate, lateral drift and an intermediate value of positive feedback of the rear wheel deflection angle.

20. The method as claimed in claim 18, in which the initial state vector allowing the initialization of the dynamic model has not been recorded, the reconstituted data used in the comparing being data reconstituted on the basis of a reconstructed initial state.

21. The method as claimed in claim 18, in which the initial state vector allowing the initialization of the dynamic model has been recorded, the method further comprising prior verification of consistency between the initial state vector recorded and the initial state vector reconstructed by comparison with threshold values of discrepancies between components of the recorded initial state vector and components of the reconstructed initial state vector.

22. The method as claimed in claim 21, in which:

when the prior verification of consistency shows a consistency, the stimulating the dynamic model with the recorded input data with a view to determining reconstituted output data is performed on the basis of the recorded initial state vector;

when the prior verification of consistency shows an inconsistency, there is undertaken, on the basis of the recorded initial state vector, a first stimulation of the dynamic model with the recorded input data so as to determine first reconstituted output data and then, on the basis of the reconstructed initial state vector, a second stimulation of the dynamic model with the recorded input data so as to determine second reconstituted output data, and then the first reconstituted output data, the second reconstituted output data, and the recorded output data are compared with a view to the consistency diagnosis.

23. A system for diagnosing operation of a system for controlling a driving parameter of a motor vehicle, using a dynamic model, comprising:

means for recording on a nonvolatile memory input and output data of the system during operation, configured to record the data with a lower sampling frequency than a system sampling frequency;

a dynamic model configured to be stimulated with the recorded input data so as to determine reconstituted output data and comparison means for comparing reconstituted output data with the recorded output data with a view to a consistency diagnosis.

24. The system as claimed in claim 23, in which the dynamic model comprises discretized dynamic equations involving, for each sampling interval, state variables of the model, the system further comprising means for reconstructing the initial state vector by inverting a system of equations comprising recorded initial data and dynamic equations corresponding to a minimum number of sampling intervals from the initial state.