Intelligent mechatronic control suspension system based on quantum soft computing

• US 20040024750A1
• Filed: 07/31/2002
• Published: 02/05/2004
• Est. Priority Date: 07/31/2002
• Status: Abandoned Application

• Current Assignee: Yamaha Hatsudoki Kabushiki Kaisha (Yamaha Corporation)
• Original Assignee: Yamaha Hatsudoki Kabushiki Kaisha (Yamaha Corporation)
• Inventors: Ulyanov, Viktor S., Ulyanov, Sergei V., Hagiwara, Takahide, Takahashi, Kazuki, Litvintseva, Ludmila, Panfilov, Sergei A.
• Application Number:
  US10/210,865
• Publication Number:
  US 20040024750A1
• Time in Patent Office: Days
• US Class Current: 707/3
• CPC Class Codes: B82Y 10/00 Nanotechnology for information processing, storage or transmission, e.g. quantum computing or single electron logic
• CPC Class Codes: G06N 10/00 Quantum computing, i.e. information processing based on quantum-mechanical phenomena

Abstract

A control system for optimizing a shock absorber having a non-linear kinetic characteristic is described. The control system uses a fitness (performance) function that is based on the physical laws of minimum entropy and biologically inspired constraints relating to mechanical constraints and/or rider comfort, driveability, etc. In one embodiment, a genetic analyzer is used in an off-line mode to develop a teaching signal. The teaching signal can be approximated online by a fuzzy controller that operates using knowledge from a knowledge base. A learning system is used to create the knowledge base for use by the online fuzzy controller. In one embodiment, the learning system uses a quantum search algorithm to search a number of solution spaces to obtain information for the knowledge base. The online fuzzy controller is used to program a linear controller.

Claims

1. A quantum search system for global optimization of a knowledge base and a robust fuzzy control algorithm design for an intelligent mechatronic control suspension system based on quantum soft computing, comprising:

a quantum genetic search module configured to develop a teaching signal for a fuzzy-logic suspension controller, said teaching signal configured to provides a desired set of control qualities over different types of roads;

a genetic analyzer module configured to produce a plurality of solutions, at least one solution for each of said different types of roads; and

a quantum search module configured to search said plurality of solutions for information to construct said teaching signal.

2. The quantum search system of claim 1, further comprising a quantum-logic feedback module for simulation of look-up tables for said fuzzy-logic suspension controller.

3. The quantum search system of claim 1, where said genetic analyzer module uses a fitness function that reduces entropy production in a suspension system controlled by said fuzzy-logic controller.

4. The quantum search system of claim 1, where said genetic analyzer module comprises a fitness function that is based on physical laws of minimum entropy and biologically inspired constraints relating to rider comfort or driveability.

5. The quantum search system of claim 1, wherein said genetic analyzer is used in an off-line mode to develop said plurality of solutions for one or more roads having different statistical characteristics.

6. The quantum search system of claim 1, wherein each of said solutions is optimized by the genetic analyzer module for a particular type of road.

7. The quantum search system of claim 1, wherein an information filter is used to filter said plurality of solution to produce a plurality of compressed solutions.

8. The quantum search system of claim 7, further comprising a fuizzy controller that approximates said teaching signal using knowledge from a knowledge base.

9. A control system for a plant comprising:

a neural network configured to control a fuzzy controller, said fuzzy controller configured to control linear controller that controls said plant;

a genetic analyzer configured to train said neural network, said genetic analyzer comprising a fitness function that reduces sensor information while reducing entropy production based on biologically-inspired constraints.

10. The control system of claim 9, wherein said genetic analyzer uses a difference between a time derivative of entropy in a control signal from a learning control unit and a time derivative of an entropy inside the plant as a measure of control performance.

11. The control system of claim 10, wherein entropy calculation of an entropy inside said plant is based on a thermodynamic model of an equation of motion for said plant that is treated as an open dynamic system.

12. The control system of claim 9, wherein said genetic analyzer generates a teaching signal for each of a plurality of solution spaces.

13. The control system of claim 9, wherein said linear control system produces a control signal based on data obtained from one or more sensors that measure said plant.

14. The control system of claim 13, wherein said plant comprises a suspension system and said cone or more sensors comprise angle and position sensors that measure angle and position of elements of the suspension system.

15. The control system of claim 9, wherein fuzzy rules used by said fuzzy controller are evolved using a kinetic model of the plant in an offline learning mode.

16. The control system of claim 15, wherein data from said kinetic model are provided to an entropy calculator that calculates input entropy production and output entropy production of the plant.

17. The control system of claim 16, wherein said input entropy production and said output entropy production are provided to a fitness function calculator that calculates a fitness function as a difference in entropy production rates constrained by one or more constraints obtained from rider preferences.

18. The control system of claim 17, wherein said genetic analyzer uses said fitness function to develop a set of training signals for an off-line control system, each training signal corresponding to a different operational environment.

19. The control system of claim 18, wherein a quantum search algorithm is used to reduce the complexity of said set of training signals by developing a universal training signal.

20. The control system of claim 9, wherein control parameters in the form of a knowledge base from an off-line control system are provided to an online control system that, using information from said knowledge base, develops a control strategy, said knowledge base developed in part by using a quantum search algorithm.

21. A method for controlling a nonlinear plant by obtaining an entropy production difference between a time derivative dS_u/dt of an entropy of the plant and a time derivative dS_c/dt of an entropy provided to the plant from a controller;

using a genetic algorithm that uses the entropy production difference as a performance function to evolve a control rule in an off-line controller;

filtering control rules from an off-line controller to reduce information content and providing filtered control rules to an online controller to control the plant.

22. The method of claim 21, further comprising using said online controller to control a damping factor of one or more shock absorbers in the vehicle suspension system.

23. The method of claim 21, further comprising evolving a control rule relative to a variable of the controller by using of a genetic algorithm, said genetic algorithm using a fitness function based on said entropy production difference.

24. A self-organizing control system, comprising:

a simulator configured to use a thermodynamic model of a nonlinear equation of motion for a plant, a fitness function module that calculates a fitness function based on an entropy production difference between a time differentiation of an entropy of said plant dS_u/dt and a time differentiation dS_c/dt of an entropy provided to the plant by a linear controller that controls the plant;

a genetic analyzer that uses said fitness function to provide a plurality of teaching signals, each teaching signal corresponding to a solution space;

a quantum search algorithm module configured to find a global teaching signal from said plurality of teaching signals;

a fuzzy logic classifier that determines one or more fuzzy rules by using a learning process and said global teaching signal; and

a fuzzy logic controller that uses said fuzzy rules to set a control variable of the linear controller.

25. The self-organizing control system of claim 24, wherein said global teaching signal is filtered to remove stochastic noise.

26. A control system comprising:

a genetic algorithm that provides a plurality of teaching signals corresponding to a plurality of spaces using a fitness function that provides a measure of control quality based on reducing production entropy in each space;

a local entropy feedback loop that provides control by relating stability of a plant and controllability of the plant; and

a quantum search module to provide a global control teaching signal from said plurality of teaching signals.

27. The control system of claim 26, wherein said quantum search module comprises a quantum associative memory.

28. The control system of claim 27, wherein said quantum associative memory is used in a quantum neural network.

29. The control system of claim 28, wherein said plant is a vehicle suspension system.

30. The control system of claim 29, wherein each of said spaces corresponds stochastic characteristics of a selected stretch of road.

31. An optimization control method for a shock absorber comprising the steps of:

obtaining a difference between a time differential of entropy inside a shock absorber and a time differential of entropy given to said shock absorber from a control unit that controls said shock absorber; and

optimizing at least one control parameter of said control unit by using a genetic algorithm and a quantum search algorithm, said genetic algorithm using said difference as a fitness function, said fitness function constrained by at least one biologically-inspired constraint.

32. The optimization control method of claim 31, wherein said time differential of said step of optimizing reduces an entropy provided to said shock absorber from said control unit.

33. The optimization control method of claim 31, wherein said control unit is comprises a fuzzy neural network, and wherein a value of a coupling coefficient for a fuzzy rule is optimized by using said genetic algorithm.

34. The optimization control method of claim 31, wherein said control unit comprises an offline module and a online control module, said method further including the steps of optimizing a control parameter based on said genetic algorithm by using said performance function, determining said control parameter of said online control module based on said control parameter and controlling said shock absorber using said online control module.

35. The optimization control method of claim 34, wherein said offline module provides optimization using a simulation model, said simulation model based on a kinetic model of a vehicle suspension system.

36. The optimization control method of claim 34, 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.

37. A method for control of a plant comprising the steps of:

calculating a first entropy production rate corresponding to an entropy production rate of a control signal provided to a model of said plant;

calculating a second entropy production rate corresponding to an entropy production rate of said model of said plant;

determining a fitness function for a genetic optimizer using said first entropy production rate and said second entropy production rate;

providing said fitness function to said genetic optimizer;

providing a teaching output from said genetic optimizer to a quantum search algorithm followed by an information filter;

providing a compressed teaching signal from said information filter to a fuzzy neural network, said fuzzy neural network configured to produce a knowledge base;

providing said knowledge base to a fuzzy controller, said fuzzy controller using an error signal and said knowledge base to produce a coefficient gain schedule; and

providing said coefficient gain schedule to a linear controller.

38. The method of claim 37, wherein said genetic optimizer minimizes entropy production under one or more constraints.

39. The method of claim 38, wherein at least one of said constraints is related to a user-perceived evaluation of control performance.

40. The method of claim 37, wherein said model of said plant comprises a model of a suspension system.

41. The method of claim 37, wherein said second control system is configured to control a physical plant.

42. The method of claim 37, wherein said second control system is configured to control a shock absorber.

43. The method of claim 37, wherein said second control system is configured to control a damping rate of a shock absorber.

44. The method of claim 37, wherein said linear controller receives sensor input data from one or more sensors that monitor a vehicle suspension system.

45. The method of claim 44, wherein at least one of said sensors is a heave sensor that measures a vehicle heave.

46. The method of claim 44, wherein at least one of said sensors is a length sensor that measures a change in length of at least a portion of said suspension system.

47. The method of claim 44, wherein at least one of said sensors is an angle sensor that measures an angle of at least a portion of said suspension system with respect to said vehicle.

48. The method of claim 44, wherein at least one of said sensors is an angle sensor that measures an angle of a first portion of said suspension system with respect to a second portion of said suspension system.

49. The method of claim 37, wherein said second control system is configured to control a throttle valve in a shock absorber.

50. A control apparatus comprising:

off-line optimization means for determining a control parameter from an entropy production rate to produce a knowledge base from a compressed teaching signal found by a quantum search algorithm; and

online control means for using said knowledge base to develop a control parameter to control a plant.