Circuit configuration for controlling a running-gear or drive system in a motor vehicle

• US 6,208,981 B1
• Filed: 01/26/1998
• Issued: 03/27/2001
• Est. Priority Date: 07/26/1995
• Status: Expired due to Fees

• Current Assignee: Siemens AG
• Original Assignee: Siemens AG
• Inventors: Hauptmann, Werner, Graf, Friedrich
• Primary Examiner(s): Powell, Mark R.
• Assistant Examiner(s): STARKS, WILBERT L
• Application Number:
  US09/013,298
• Time in Patent Office: 1,156 Days
• Field of Search: 706/2, 706/16, 706/25, 706/23, 431/12, 431/14, 701/27, 701/59, 701/40, 701/44, 701/57, 701/77, 701/98, 701/106
• US Class Current: 706/2
• CPC Class Codes: B60G 17/018 characterised by the use of a specific signal treatment or control method
• CPC Class Codes: B60G 17/0195 characterised by the regulation being combined with other vehicle control systems conjoint control of vehicle sub-units including control of suspension systems B60W10/22
• CPC Class Codes: B60G 2600/1878 Neural Networks
• CPC Class Codes: B60G 2600/1879 Fuzzy Logic Control
• CPC Class Codes: B60T 2220/02 Driver type; Driving style; Driver adaptive features
• CPC Class Codes: B60T 8/174 characterised by using special control logic, e.g. fuzzy logic , neural computing
• CPC Class Codes: B60W 2050/0014 Adaptive controllers
• CPC Class Codes: B60W 2050/0057 Frequency analysis, spectral techniques or transforms
• CPC Class Codes: B60W 50/00 Details of control systems for road vehicle drive control not related to the control of a particular sub-unit , e.g. process diagnostic or vehicle driver interfaces
• CPC Class Codes: B62D 6/00 Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits means for initiating a change in direction B62D1/00; steering valves B62D5/06; combined with means for inclining the vehicle body or wheels on bends B62D9/00
• CPC Class Codes: F16H 2061/0081 Fuzzy logic
• CPC Class Codes: F16H 2061/0084 Neural networks
• CPC Class Codes: F16H 2061/0087 Adaptive control, e.g. the control parameters adapted by learning
• CPC Class Codes: F16H 61/0213 characterised by the method for generating shift signals
• CPC Class Codes: G05B 13/0285 using neural networks and fuzzy logic

Abstract

Motor vehicle sensor signals are evaluated by a fuzzy system, which generates control signals for a system device of the motor vehicle—for example an automatic transmission, active suspension, speed stabilization, power-steering assistance, or traction control. The fuzzy system is connected to a neural network, which evaluates the sensor signals and reference data from a recording of driving data of the motor vehicle. The neural network optimizes the rule base of the fuzzy system. During a driving operation, the fuzzy system generates on-line signals categorizing the respective driving situation, and thus makes possible intelligent, time-adaptive, driving-situation-dependent control. The fuzzy system and the neural network each contain a classification system which can be reciprocally converted by a correspondence-maintaining bidirectional transformation.

Claims

1. A circuit configuration for controlling a system device in a motor vehicle, comprising:

a fuzzy system receiving sensor signals from a motor vehicle, said fuzzy system being programmed to evaluate the sensor signals, to determine a driving situation, to generate, during a driving operation of the motor vehicle, signals classifying a respective driving situation, and to generate driving-situation-dependent control signals for a system device in the motor vehicle;

a neural network connected to said fuzzy system, said neural network being programmed to evaluate the sensor signals and reference data from a recording of driving data of the motor vehicle, said neural network generating and optimizing said fuzzy system; and

a preprocessing circuit connected to said fuzzy system, said preprocessing circuit filtering and smoothing the sensor signals and/or control signals prior to an evaluation thereof in said fuzzy system, and wherein a linkage effects a dimensional reduction of the sensor signals.

2. The circuit configuration according to claim 1, which further comprises a signal memory connected to said fuzzy system, said signal memory storing sensor signals prior to an evaluation thereof by said fuzzy system.

3. The circuit configuration according to claim 1, which further comprises a signal memory connected to said fuzzy system, said signal memory storing the control signals.

4. The circuit configuration according claim 1, which further comprises a database storing video recordings of measurement trips taken by the motor vehicle, and a reference data memory through which data contained in said database are delivered to said neural network.

5. The circuit configuration according to claim 1, wherein said neural network is further programmed to adapt a form and location of membership functions of input and output data, and a rule base of said fuzzy system.

6. The circuit configuration according to claim 1, wherein said fuzzy system and said neural network have functionally equivalent, respective input and output behavior.

7. The circuit configuration according to claim 1, wherein each of said fuzzy system and said neural network has a classification system, and the classification system of one is convertible into the classification system of another by a correspondence-maintaining bidirectional transformation.