Intelligent electronically-controlled suspension system based on soft computing optimizer
First Claim
1. An optimization control method for controlling an electronically-controlled suspension system, comprising:
- using a controller genetic algorithm to develop an optimzed teaching signal, said genetic algorithm having a fitness function that computes a difference between a time differential of entropy inside a shock absorber and/or inside the whole vehicle including passengers and/or other load and a time differential of entropy in a control signal provided to said shock absorber from an fuzzy controller that controls said shock absorber while said shock absorber is being perturbed by a road signal;
using first genetic algorithm to optimize a fuzzy inference engine to develop a knowledge base structure by optimizing at least one of, a number of input variables of said knowledge base, a number of output variables of said knowledge base, a type of fuzzy inference model used by said fuzzy inference engine, and a preliminary type of membership function;
using said teaching/training signal to learn/train said fuzzy inference engine by setting knowledge paramteres in said knowledge base; and
providing said knowledge base to said fuzzy controller to control said shock absorber.
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Abstract
A Soft Computing (SC) optimizer for designing a Knowledge Base (KB) to be used in a control system for controlling a suspension system is described. The SC optimizer includes a fuzzy inference engine based on a Fuzzy Neural Network (FNN). The SC Optimizer provides Fuzzy Inference System (FIS) structure selection, FIS structure optimization method selection, and teaching signal selection and generation. The user selects a fuzzy model, including one or more of: the number of input and/or output variables; the type of fuzzy inference model (e.g., Mamdani, Sugeno, Tsukamoto, etc.); and the preliminary type of membership functions. A Genetic Algorithm (GA) is used to optimize linguistic variable parameters and the input-output training patterns. A GA is also used to optimize the rule base, using the fuzzy model, optimal linguistic variable parameters, and a teaching signal. The GA produces a near-optimal FNN. The near-optimal FNN can be improved using classical derivative-based optimization procedures. The FIS structure found by the GA is optimized with a fitness function based on a response of the actual suspension system model of the controlled suspension system. The SC optimizer produces a robust KB that is typically smaller that the KB produced by prior art methods.
78 Citations
98 Claims
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1. An optimization control method for controlling an electronically-controlled suspension system, comprising:
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using a controller genetic algorithm to develop an optimzed teaching signal, said genetic algorithm having a fitness function that computes a difference between a time differential of entropy inside a shock absorber and/or inside the whole vehicle including passengers and/or other load and a time differential of entropy in a control signal provided to said shock absorber from an fuzzy controller that controls said shock absorber while said shock absorber is being perturbed by a road signal;
using first genetic algorithm to optimize a fuzzy inference engine to develop a knowledge base structure by optimizing at least one of, a number of input variables of said knowledge base, a number of output variables of said knowledge base, a type of fuzzy inference model used by said fuzzy inference engine, and a preliminary type of membership function;
using said teaching/training signal to learn/train said fuzzy inference engine by setting knowledge paramteres in said knowledge base; and
providing said knowledge base to said fuzzy controller to control said shock absorber. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 98)
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16. A method for control of a suspension system comprising the steps of:
- determining a fitness function for a teaching signal genetic optimizer using a first entropy production rate and a second entropy production rate;
providing said fitness function to said teaching signal genetic optimizer;
providing a teaching signal output from said teaching signal genetic optimizer to an information filter;
providing a compressed teaching signal from said information filter to a soft computing optimizer for optimizing a structure of a knowledge base for a fuzzy neural network, 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 (17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56)
- determining a fitness function for a teaching signal genetic optimizer using a first entropy production rate and a second entropy production rate;
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57. A control apparatus comprising:
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off-line optimization means for determining a control parameter from an entropy production rate;
soft computing optimizer means to configure a knowledge base;
training means for training said knowledge base; and
online control means for using said knowledge base to develop a control parameter to control a suspension system.
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58. A soft computing optimizer for a suspension control system, comprising:
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an off-line optimizer for developing a training signal from data obtained by providing at least one road signal disturbance to a first suspension system;
a soft computing optimizer configured to use said training signal to find a structure for a knowledge base;
a training optimizer configured to generate knowledge base corresponding to said structure; and
an online control system configured to use said knowledge base to develop a control parameter to control a second suspension system. - View Dependent Claims (59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97)
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84. A self-organizing control system for optimization of a knowledge base, comprising:
an fuzzy logic classifier configured to optimize a structure of a knowledge base for a fuzzy inference system;
a genetic analyzer configured to develop a teaching signal for said fuzzy-logic classifier, said teaching signal configured to provide a desired set of control qualities, said genetic analyzer using chromosomes, a portion of said chromosomes being step coded; and
a PID controller with discrete constraints, said PID controller configured to receive a gain schedule from said fuzzy controller.
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90. A control system for a suspension system, comprising:
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a fuzzy logic classifier system configured to optimize a structure of a knowledge base for a fuzzy controller, said fuzzy controller configured to control a linear controller with discrete constraints; and
a genetic analyzer configured to provide a training signal to said fuzzy logic classifier, said genetic analyzer configured to use step-coded chromosomes.
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Specification