WO2001061533A3 - Method and apparatus for training a state machine - Google Patents

Method and apparatus for training a state machine Download PDF

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Publication number
WO2001061533A3
WO2001061533A3 PCT/US2001/001822 US0101822W WO0161533A3 WO 2001061533 A3 WO2001061533 A3 WO 2001061533A3 US 0101822 W US0101822 W US 0101822W WO 0161533 A3 WO0161533 A3 WO 0161533A3
Authority
WO
WIPO (PCT)
Prior art keywords
state
derivative
variables
variable
equations
Prior art date
Application number
PCT/US2001/001822
Other languages
French (fr)
Other versions
WO2001061533A2 (en
Inventor
Ralph E Rose
Original Assignee
Ralph E Rose
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Ralph E Rose filed Critical Ralph E Rose
Priority to AU2001234491A priority Critical patent/AU2001234491A1/en
Publication of WO2001061533A2 publication Critical patent/WO2001061533A2/en
Publication of WO2001061533A3 publication Critical patent/WO2001061533A3/en

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Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance

Abstract

A technique is disclosed for adjusting the parameters in the state equations governing the behavior of a system of state variables. By proper adjustment of the parameters in the state equations, the behavior of the state variables can be modified so that the system behaves as desired. The key to using this technique is the development of the concept of derivative variables. The derivative variables are variables processed by their own set of state equations derived from the original set of state equations. For each state variable in the original system and each parameter whose value is to be adjust there exist a unique derivative variable. The value of the each derivative variable represents the instantaneous value of the derivative of a particular ordinary state variable with a particular parameter. Two matrix techniques are disclosed that can be used to increase the training rate.
PCT/US2001/001822 2000-02-18 2001-01-18 Method and apparatus for training a state machine WO2001061533A2 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU2001234491A AU2001234491A1 (en) 2000-02-18 2001-01-18 Method and apparatus for training a state machine

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US50713100A 2000-02-18 2000-02-18
US09/507,131 2000-02-18
US69300300A 2000-10-20 2000-10-20
US09/693,003 2000-10-20

Publications (2)

Publication Number Publication Date
WO2001061533A2 WO2001061533A2 (en) 2001-08-23
WO2001061533A3 true WO2001061533A3 (en) 2002-06-20

Family

ID=27055727

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2001/001822 WO2001061533A2 (en) 2000-02-18 2001-01-18 Method and apparatus for training a state machine

Country Status (3)

Country Link
AU (1) AU2001234491A1 (en)
CA (1) CA2331477A1 (en)
WO (1) WO2001061533A2 (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108681252A (en) * 2018-05-15 2018-10-19 浙江工业大学 A kind of Multi-arm robots finite time parameter identification and position synchronization control method based on mean value coupling

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9886285B2 (en) 2015-03-31 2018-02-06 Western Digital Technologies, Inc. Communication interface initialization
CN109465829B (en) * 2018-12-12 2021-05-04 南京工程学院 Industrial robot geometric parameter identification method based on transformation matrix error model

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5283855A (en) * 1990-11-22 1994-02-01 Ricoh Company, Ltd. Neural network and method for training the neural network
US5349541A (en) * 1992-01-23 1994-09-20 Electric Power Research Institute, Inc. Method and apparatus utilizing neural networks to predict a specified signal value within a multi-element system
US5517432A (en) * 1994-01-31 1996-05-14 Sony Corporation Of Japan Finite state machine transition analyzer
US5903886A (en) * 1996-04-30 1999-05-11 Smartlynx, Inc. Hierarchical adaptive state machine for emulating and augmenting software
US5974434A (en) * 1997-10-07 1999-10-26 Ralph E. Rose Method and apparatus for automatically tuning the parameters of a feedback control system

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5283855A (en) * 1990-11-22 1994-02-01 Ricoh Company, Ltd. Neural network and method for training the neural network
US5349541A (en) * 1992-01-23 1994-09-20 Electric Power Research Institute, Inc. Method and apparatus utilizing neural networks to predict a specified signal value within a multi-element system
US5517432A (en) * 1994-01-31 1996-05-14 Sony Corporation Of Japan Finite state machine transition analyzer
US5903886A (en) * 1996-04-30 1999-05-11 Smartlynx, Inc. Hierarchical adaptive state machine for emulating and augmenting software
US5974434A (en) * 1997-10-07 1999-10-26 Ralph E. Rose Method and apparatus for automatically tuning the parameters of a feedback control system

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108681252A (en) * 2018-05-15 2018-10-19 浙江工业大学 A kind of Multi-arm robots finite time parameter identification and position synchronization control method based on mean value coupling
CN108681252B (en) * 2018-05-15 2021-01-01 浙江工业大学 Multi-mechanical-arm system finite time parameter identification and position synchronization control method

Also Published As

Publication number Publication date
AU2001234491A1 (en) 2001-08-27
WO2001061533A2 (en) 2001-08-23
CA2331477A1 (en) 2001-08-18

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