Electronic controller using genetic evolution techniques suitable for controlling a motor

• US 6,032,139 A
• Filed: 09/29/1997
• Issued: 02/29/2000
• Est. Priority Date: 09/27/1996
• Status: Expired due to Fees

• Current Assignee: Yamaha Hatsudoki Kabushiki Kaisha (Yamaha Corporation)
• Original Assignee: Yamaha Hatsudoki Kabushiki Kaisha (Yamaha Corporation)
• Inventors: Yamaguchi, Masashi, Kamihira, Ichikai
• Primary Examiner(s): Hafiz, Tariq R.
• Assistant Examiner(s): Pender, Jr., Michael J.
• Application Number:
  US08/939,132
• Time in Patent Office: 883 Days
• Field of Search: 706/13, 364/148.03
• US Class Current: 706/13
• CPC Class Codes: F02D 41/1401 characterised by the control or regulation method F02D41/1473, F02D41/1477 take precedence
• CPC Class Codes: F02D 41/1405 Neural network control
• CPC Class Codes: G05B 13/0265 the criterion being a learning criterion
• CPC Class Codes: G05B 13/027 using neural networks only
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming

Abstract

An evolutionary control for a subject such as an engine installed in a vehicle is conducted by the steps of: selecting coefficients, as genes, affecting the control characteristics of the control system; creating plural control units as chromosomes, each being constituted by plural genes; expressing the genes of each chromosome as control characteristics by controlling the subject using the control system having the control characteristics; selecting at least one chromosome from the created chromosomes based on the control characteristics expressed by the genes in view of the user'"'"'s preference; and causing the at least one chromosome to evolve using a genetic algorithm, thereby obtaining control characteristics suitable for the user. In this method, the characteristics of the product can effectively be adjusted after its purchase based on the user'"'"'s preference.

Claims

1. A method for controlling performance of a motor used by a user, which motor is operable by causative signals, the performance of which motor is indicatable by indicative signals, said motor being controlled by a control system provided with said motor and comprising:

(i) a base value output unit to output a base value of a causative signal when receiving pre-selected signals, said base value output unit preventing said motor from malfunctioning; and

(ii) an evolution unit comprising a control module programmed to output and add a compensation value to the base value of the causative signal when receiving pre-selected signals, wherein the input-output relationship of the control module is regulated by coefficients;

said evolution unit provided with a genetic algorithm unit programmed to select fitted coefficients based on a selection signal when using coefficients as genes, wherein the selected fitted coefficients replace the coefficients used in the control module to update its input-output relationship;

said method being conducted without discontinuing performance of said motor comprising the steps of;

(a) outputting the causative signals from the base value;

(b) outputting and adding a compensation value to the base value of the causative signal to operate said motor;

(c) selecting fitted coefficients by the genetic algorithm unit based on a selected selection signal, said selection signal inputted by the user in response to the performance of said motor based on its indicative signals while said motor is in operation; and

(d) replacing the coefficients used in the control module to update its input-output relationship to control said motor whereby said control system is updated on a real time basis to adapt performance of said motor to the user.

2. The method according to claim 1, wherein the selection step further comprises a step of preliminaily selecting fitted coefficients based on a pre-set target value prior to the selection based on the selection signal.

3. The method according to claim 1, wherein the control module includes a neural network, the input-output relationship of which is regulated by coupling coefficients, said coupling coefficients being used as genes at the genetic algorithm units.

4. The method according to claim 1, wherein the control system further comprises a learning unit between the evolution unit and the base value output unit, said learning unit having a learning function which copies the input-output relationship of the control module, said method further comprising copying the input-output relationship of the control module and outputting a compensation value.

5. The method according to claim 1, wherein the user'"'"'s selection is conducted by using an indication monitor which indicates the performance of said motor, wherein the user selects preferable performance indicated on the monitor.

6. The method according to claim 1, wherein said motor is an engine for a vehicle, wherein the causative signal comprises fuel injection quantity and the pre-selected signals comprise engine revolutions and electric-throttle angle.