Intelligent mechatronic control suspension system based on soft computing
First Claim
1. 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, said genetic algorithm using said difference as a fitness function, said fitness function constrained by at least one biologically-inspired constraint.
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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. An information filter is used to filter the teaching signal to produce a compressed teaching signal. The compressed teaching signal can be approximated online by a fuzzy controller that operates using knowledge from a knowledge base. In one embodiment, the control system includes a learning system, such as a neural network that is trained by the compressed training signal. The learning system is used to create a knowledge base for use by an online fuzzy controller. The online fuzzy controller is used to program a linear controller.
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Citations
20 Claims
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1. An optimization control method for a shock absorber comprising the steps of:
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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, said genetic algorithm using said difference as a fitness function, said fitness function constrained by at least one biologically-inspired constraint. - View Dependent Claims (2, 3, 4, 5, 6)
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7. 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 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. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
- calculating a first entropy production rate corresponding to an entropy production rate of a control signal provided to a model of said plant;
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20. 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; and
online control means for using said knowledge base to develop a control parameter to control a plant.
- off-line optimization means for determining a control parameter from an entropy production rate to produce a knowledge base from a compressed teaching signal; and
Specification