Claims
- 1. A control system for optimizing the performance of a vehicle suspension system by controlling the damping factor of one or more shock absorbers, comprising:
a fuzzy neural network having a knowledge base trained by using a teaching signal; one or more sensors to sense heave acceleration and produce a heave acceleration signal; a lowpass filter to remove high-frequency noise from said heave acceleration signal to produce a filtered heave acceleration signal for said fuzzy neural network; an integrator to produce a velocity signal from said filtered heave acceleration signal for said fuzzy neural network; a bandpass filter to produce a bandpass filtered velocity signal for said fuzzy neural network; a high filter to produce a highpass filtered velocity signal for said fuzzy neural network; and a Fourier transform to extract frequency components of said velocity signal for said fuzzy neural network.
- 2. The control system of claim 1, wherein said bandpass filter selects frequency components related to natural frequencies of the vehicle body.
- 3. The control system of claim 1, wherein said highpass filter selects frequency components above 5 Hertz.
- 4. The control system of claim 1, wherein said highpass filter selects frequency components related to wheel hops.
- 5. The control system of claim 1, wherein said Fourier transform provides frequency components around 1 Hertz.
- 6. The control system of claim 1, wherein said Fourier transform filter selects frequency components related to road roughness.
- 7. The control system of claim 1, wherein said teaching signal is generated by applying a learning road signal to a model of said suspension system and optimizing damping factor of said shock absorbers by a genetic algorithm.
- 8. The control system of claim 7, wherein a fitness function used by said genetic algorithm is configured to reduce relatively low frequency components of pitch angular acceleration to provide better stability.
- 9. The control system of claim 7, wherein a fitness function used by said genetic algorithm is configured to reduce relatively high frequency components of heave acceleration to provide better riding comfort.
- 10. The control system of claim 7, wherein a fitness function used by said genetic algorithm is configured to reduce relatively low frequency components of pitch angular acceleration and to reduce relatively high frequency components of heave acceleration.
- 11. An optimization control method for controlling a vehicle suspension system comprising:
generating a teaching signal by:
applying a road signal to a model of a vehicle and suspension system; and using a genetic optimizer to optimize damping forces of a plurality of shock absorbers in said suspension system disturbed by said road signal; generating a knowledge base for a fuzzy neural network by;
filtering a heave acceleration signal portion of said teaching signal to generate a plurality of inputs for said fuzzy neural network; developing an error signal by comparing damper control values in said teaching signal to damper control values produced by said fuzzy neural network; and configuring said knowledge base to reduce said error signal; and providing said knowledge base to a fuzzy neural network in a fuzzy controller to control said vehicle suspension system.
- 12. The optimization control method of claim 11, wherein said genetic optimizer uses a fitness function configured to reduce relatively low frequency components of pitch angular acceleration and to reduce relatively high frequency components of heave acceleration.
- 13. The optimization control method of claim 11, wherein said genetic optimizer uses a fitness function configured to reduce relatively low frequency components of pitch angular acceleration.
- 14. The optimization control method of claim 1, wherein said control unit comprises a learning control module and an actual control module, said method further including the steps of optimizing a control parameter based on said genetic algorithm by using a performance function, determining a control parameter of said actual control module based on said control parameter and controlling said shock absorber using said actual control module.
- 15. The optimization control method of claim 14, wherein said step of optimization of said learning control unit is performed using a simulation model, said simulation model based on a kinetic model of a vehicle suspension system.
- 16. The optimization control method of claim 14, wherein said shock absorber is arranged to alter a damping force by altering a cross-sectional area of an oil passage, and said control unit controls a throttle valve to thereby adjust said cross-sectional area of said oil passage.
- 17. A method for control of a plant comprising:
applying a road signal to a model of a vehicle and suspension system and using a genetic optimizer in a first control system to optimize damping forces of a plurality of shock absorbers in said suspension system disturbed by said road signal; generating a knowledge base for a fuzzy neural network by filtering a heave acceleration signal portion of said teaching signal to generate a plurality of inputs for said fuzzy neural network and configuring said knowledge base by comparing outputs of said fuzzy neural network to at least a portion of said training signal; and providing said knowledge base to a second control system to control said vehicle suspension system.
- 18. The method of claim 17, wherein said first control system comprises a heave signal input.
- 19. The method of claim 17, wherein said second control system comprises a heave signal input.
- 20. The method of claim 17, wherein said model comprises a dynamic model.
- 21. The method of claim 17, wherein said second control system receives sensor input data from one or more acceleration sensors.
- 22. The method of claim 17, wherein said filtering comprises lowpass filtering, bandpass filtering, and highpass filtering.
- 23. The method of claim 17, wherein said filtering comprises applying a Fourier transform to portions of said heave acceleration signal.
- 24. The control system of claim 17, wherein said filtering comprises bandpass filtering to select frequency components related to natural frequencies of the vehicle body.
- 25. The control system of claim 17, wherein said filtering comprises lowpass filtering to remove noise followed by highpass filtering to select frequency components above 5 Hertz.
- 26. The control system of claim 17, wherein said filtering comprises highpass filtering to select frequency components related to wheel hops.
- 27. The control system of claim 17, wherein said filtering comprises lowpass filtering to remove noise followed by Fourier transforming to provide frequency components around 1 Hertz.
- 28. The control system of claim 17, wherein said filtering comprises Fourier transforming to select frequency components related to road roughness.
- 29. The control system of claim 17, wherein said filtering comprises integrating an acceleration signal to produce a velocity signal followed by highpass filtering to select frequency components related to wheel hops.
- 30. The control system of claim 17, wherein said filtering comprises integrating an acceleration signal to produce a velocity signal followed by bandpass filtering to select frequency components related to natural frequencies of the vehicle body.
- 31. A control system, comprising:
a fuzzy controller configured to control damping coefficients of shock absorbers in a vehicle suspension system; at least one sensor to provide sensor data; and means for filtering said sensor data to produce a plurality of input signals for a fuzzy neural network in said fuzzy controller.
- 32. The control system of claim 31, wherein said means for filtering comprises at least one of an integrator, a differentiator, a low-pass filter, a band-pass filter, and a high-pass filter.
- 33. The control system of claim 31, wherein said means for filtering comprises a Fourier transform process for extracting one or more focused frequency components.
- 34. The control system of claim 31, wherein said means for filtering comprises band-pass filtering corresponding to a resonance frequency of a heave movement, a pitch movement, or a roll movement.
- 35. A control system for optimizing the performance of a vehicle suspension system by controlling the damping factor of one or more shock absorbers, comprising:
a fuzzy neural network having a knowledge base trained by using a teaching signal; one or more sensors to sense heave acceleration and produce a heave acceleration signal; a lowpass filter to remove high-frequency noise from said heave acceleration signal to produce a filtered heave acceleration signal for said fuzzy neural network; an integrator to produce a velocity signal from said filtered heave acceleration signal for said fuzzy neural network; a bandpass filter to produce a bandpass filtered velocity signal for said fuzzy neural network; a high filter to produce a highpass filtered velocity signal for said fuzzy neural network; and a Fourier transform to extract frequency components of said filtered heave acceleration signal for said fuzzy neural network.
- 36. The control system of claim 35, wherein said bandpass filter selects frequency components related to natural frequencies of the vehicle body.
- 37. The control system of claim 35, wherein said highpass filter selects frequency components above 5 Hertz.
- 38. The control system of claim 35, wherein said highpass filter selects frequency components related to wheel hops.
- 39. The control system of claim 35, wherein said Fourier transform provides frequency components around 1 Hertz.
- 40. The control system of claim 35, wherein said Fourier transform filter selects frequency components related to road roughness.
- 41. The control system of claim 35, wherein said teaching signal is generated by applying a learning road signal to a model of said suspension system and optimizing damping factor of said shock absorbers by a genetic algorithm.
- 42. The control system of claim 41, wherein a fitness function used by said genetic algorithm is configured to reduce relatively low frequency components of pitch angular acceleration to provide better stability.
- 43. The control system of claim 41, wherein a fitness function used by said genetic algorithm is configured to reduce relatively high frequency components of heave acceleration to provide better riding comfort.
- 44. The control system of claim 41, wherein a fitness function used by said genetic algorithm is configured to reduce relatively low frequency components of pitch angular acceleration and to reduce relatively high frequency components of heave acceleration.
REFERENCE TO RELATED APPLICATION
[0001] The present application claims priority benefit of U.S. Provisional Application No. 60/410,741, filed Sep. 13, 2002, titled “FUZZY CONTROLLER WITH A REDUCED NUMBER OF SENSORS”, the entire contents of which is hereby incorporated by reference.
Provisional Applications (1)
|
Number |
Date |
Country |
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60410741 |
Sep 2002 |
US |