Speed adaptation using Neuro fuzzy approach

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Speed adaptation using Neuro fuzzy approach

  1. 1. PROJECT ON Intelligent Speed Adaptation Using Neuro-Fuzzy Controller GUIDED BY PROF. S S WANKHEDE Kshetrimayum Jevel Singh Lucky Amesar Nisha Kanoo Praful Kambe G.H.RAISONI COLLEGE OF ENGINEERING Department of Electronics & Telecommunication
  2. 2. OBJECTIVES This Project aims towards design of Hybrid Controller using Neuro- Fuzzy technique for Longitudinal controlling of Automotive System
  3. 3. <ul><li>Era of Automation </li></ul><ul><li>Automation in vehicle </li></ul>Introduction
  4. 4. Why do we need controller in vehicles? <ul><li>WHO report on road traffic injury prevention states that </li></ul><ul><li>1.2 million people are killed in road accident every year </li></ul><ul><li>50 millions are injured </li></ul><ul><li>Figure will increase upto 65% in next 20 years </li></ul><ul><li>Global cost of road crashes and injuries is about US $ 518 billions per year. </li></ul>But the basic reason for all this is …….. “ Human error”
  5. 5. WHO report figure on Road accidents …..
  6. 6. PROBLEM ANALYSIS <ul><li>Driving as a continuous decision making process </li></ul><ul><li>Earlier speed adaptation systems with limitations </li></ul><ul><li>Modified System </li></ul>HUMAN APPROACH Efficient in Decision making NEURAL NETWORKS FUZZY LOGIC
  7. 7. HYBRID CONTROLLER Neural Network + Fuzzy Logic Good for learning. Not good for human to interpret its internal representation. <ul><li>Supervised leaning </li></ul><ul><li>Unsupervised learning </li></ul><ul><li>Reinforcement learning </li></ul>Human reasoning scheme. Fuzzy rules and membership functions are subjective. <ul><li>Readable Fuzzy rules </li></ul><ul><li>Interpretable </li></ul>
  8. 8. HYBRID CONTROLLER Cont…. Neural Network + Fuzzy Logic Good for learning. Not good for human to interpret its internal representation. <ul><li>Supervised leaning </li></ul><ul><li>Unsupervised learning </li></ul><ul><li>Reinforcement learning </li></ul>Human reasoning scheme. Fuzzy rules and membership functions are subjective. <ul><li>Readable Fuzzy rules </li></ul><ul><li>Interpretable </li></ul>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.
  9. 9. <ul><li>Vehicle Controller based on </li></ul><ul><li>“ Generic self organizing Fuzzy-Neural Network” </li></ul><ul><li>Mapped by </li></ul><ul><li> “ Yager’s Inference Scheme” </li></ul>VEHICLE CONTROLLER <ul><li>It is a fuzzy-Neural network which uses Yager’s inference scheme to interpret fuzzy relations. </li></ul>
  10. 10. Yager’s Inference Scheme <ul><li>It is an extension of modus pones rule which is nothing but similar to implication & is also called as affirmative mode </li></ul><ul><li>It can be stated as </li></ul><ul><li>If A is true </li></ul><ul><li>and </li></ul><ul><li> A B </li></ul><ul><li>then B is true. </li></ul>
  11. 11. STRUCTURE OF GenSOFNN Layer 1 input linguistic nodes Layer 2 input term nodes Layer 3 rule nodes Layer 4 Output term node Layer 5 output linguistic nodes
  12. 12. Fuzzifier Inference Engine Defuzzifier STRUCTURE OF GenSOFNN contd.. Layer 1 input linguistic nodes Layer 2 input term nodes Layer 3 rule nodes Layer 4 Output term node Layer 5 output linguistic nodes
  13. 13. antecedent consquent STRUCTURE OF GenSOFNN contd… DIC Technique` is used Layer 1 input linguistic nodes Layer 2 input term nodes Layer 3 rule nodes Layer 4 Output term node Layer 5 output linguistic nodes
  14. 14. Adaptation to vehicle controller GenSoYager GenSoYager anticipation speed Throttle speed anticipation Brake A.) Implementation of Longitudinal Control
  15. 15. Adaptation to vehicle controller B.) Training of GenSoYager system Human Driver Driving Simulator Log File Visual Feedback Action Action
  16. 16. INPUTS TO VEHICLE CONTROLLER INPUTS Speed Anticipation <ul><li>It’s a linear variable. </li></ul><ul><li>It depends upon speed limit of vehicle </li></ul>1. It depends upon the curve & distance from curve. 2. Calculated by using log file.
  17. 17. Calculation of anticipation factor speed distance Anticipation algorithm anticipation
  18. 18. IMPLEMENTATION Problem analysis HUMAN approach Decision making GENSOFNN Training using Error back propagation algorithm & log file Provided with Fuzzy set of rules to interpret
  19. 19. ADVANTAGES <ul><li>Comparatively better control. </li></ul><ul><li>Anticipation Factor doesn’t vary . </li></ul><ul><li>Chances of Road mishaps reduces. </li></ul>
  20. 20. DRAWBACKS <ul><li>TORCS,an open source simulator which is selected for the simulation. </li></ul><ul><li>It doesn’t take into account the action of centripetal force during the car Slipping over a turning. </li></ul><ul><li>For this we have to depend upon the reliability of the system to control the vehicle </li></ul>
  21. 21. Scope of Work <ul><li>Like longitudinal control lateral control can also be implemented by using the concept of anticipation. </li></ul>
  22. 22. References <ul><li>M. Peden, R. Scurfield, D. Sleet, et al. World Report on road traffic injury prvention. World Health Organisation, 2004 </li></ul><ul><li>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. </li></ul><ul><li>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 </li></ul>
  23. 23. THANKING YOU QUESTIONS?

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