Post on 21-Nov-2014
description
transcript
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Kshetrimayum Jevel Singh
Lucky Amesar
Nisha Kanoo
Praful Kambe
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This Project aims towards design of Hybrid Controller using Neuro- Fuzzy technique for Longitudinal controlling of Automotive System
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Introduction
Era of Automation
Automation in vehicle
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Why do we need controller in vehicles?
WHO report on road traffic injury prevention states that
1.2 million people are killed in road accident every year
50 millions are injured
Figure will increase upto 65% in next 20 years
Global cost of road crashes and injuries is about US $ 518 billions per year.
But the basic reason for all this is ……..
“Human error”
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WHO report figure on Road accidents …..
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PROBLEM ANALYSIS
Driving as a continuous decision making process
Earlier speed adaptation systems with limitations
Modified System
HUMANAPPROACH
Efficient in
Decision making
NEURALNETWORKS
FUZZY LOGIC
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NeuralNetwork + Fuzzy
LogicGood for learning.
Not good for human to interpret its internal representation.
• Supervised leaning• Unsupervised learning• Reinforcement learning
Human reasoning scheme.
Fuzzy rules and membership functions are subjective.
• Readable Fuzzy rules• Interpretable
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Cont….
NeuralNetwork + Fuzzy
LogicGood for learning.
Not good for human to interpret its internal representation.
• Supervised leaning• Unsupervised learning• Reinforcement learning
Human reasoning scheme.
Fuzzy rules and membership functions are subjective.
• Readable Fuzzy rules• Interpretable
A Neuro-fuzzy system is a fuzzy system that uses a
learning algorithm derived from or inspired by
neural network theory to determine its parameters
by processing data samples.
A Neuro-fuzzy system is a fuzzy system that uses a
learning algorithm derived from or inspired by
neural network theory to determine its parameters
by processing data samples.
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Vehicle Controller based on “Generic self organizing Fuzzy-Neural
Network” Mapped by
“Yager’s Inference Scheme” It is a fuzzy-Neural network which uses Yager’s inference scheme to interpret fuzzy relations.
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Yager’s Inference Scheme It is an extension of modus pones rule which is
nothing but similar to implication & is also called as affirmative mode
It can be stated as If A is true and
A B then B is true.
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STRUCTURE OF GenSOFNN
Layer 1input
linguistic nodes
Layer 2input term nodes
Layer 3rule
nodes
Layer 4Output term node
Layer 5output
linguistic nodes
1x 2x nx
1y 1y myˆmy
121x 2x nx
1y 1y myˆmy
Layer 1input
linguistic nodes
Layer 2input term nodes
Layer 3rule
nodes
Layer 4Output term node
Layer 5output
linguistic nodes
Fuzzifier
Inference Engine
Defuzzifier
STRUCTURE OF GenSOFNN contd..
131x 2x nx
1y 1y myˆmy
Layer 1input
linguistic nodes
Layer 2input term nodes
Layer 3rule
nodes
Layer 4Output term node
Layer 5output
linguistic nodes
antecedent
consquent
STRUCTURE OF GenSOFNN contd…DIC Technique`
is used
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Adaptation to vehicle controller
GenSoYager GenSoYager
anticipationspeed
Throttle
speed anticipation
Brake
A.) Implementation of Longitudinal Control
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Adaptation to vehicle controller
B.) Training of GenSoYager system
Human Driver Driving Simulator Log File
Visual Feedback
ActionAction
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INPUTS TO VEHICLE CONTROLLER
INPUTS
Speed Anticipation
1. It’s a linear variable.2. It depends upon speed
limit of vehicle
1. It depends upon the curve & distance from curve.2. Calculated by using log file.
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Calculation of anticipation factor
speed distance
Anticipation
algorithm
anticipation
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IMPLEMENTATIONProblem analysis
HUMAN approach
Decision making
GENSOFNN
Training using Error back propagation algorithm & log file
Provided with Fuzzy set of rules to interpret
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ADVANTAGES
Comparatively better control. Anticipation Factor doesn’t vary . Chances of Road mishaps reduces.
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DRAWBACKS
TORCS,an open source simulator which is selected for the simulation.
It doesn’t take into account the action of centripetal force during the car Slipping over a turning.
For this we have to depend upon the reliability of the system to control the vehicle
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Scope of Work
Like longitudinal control lateral control can also be implemented by using the concept of anticipation.
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References M. Peden, R. Scurfield, D. Sleet, et al. World Report
on road traffic injury prvention. World Health Organisation, 2004
M. Pasquier, C. Quek, and M. Toh. Fuzzylot: A Novel self-organising Fuzzy-Neural rule-based pilot system for automated vehicles. Neural networks, vol. 14, no. 8, pp. 1099-1112, Oct. 2001.
W.L.Tung, and C.Quek. GenSoFNN: A Generic self-organising Fuzzy-Neural Network . IEEE Transactions on Neural Networks, vol. 13, no.5, pp.1075-1086, 2002
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