1. Fuzzy Logic Controlled Brakes for Trains Erik Lee May 2, 2012 EE 525.770 Intelligent Algorithms Dr. Neil F. Palumbo

2. FUZZY LOGIC BASED AUTOMATIC BRAKING SYSTEM IN TRAINS G. Sankar and S. Saravana Kumar Bannari Amman Institute of Technology Sathyamangalam, Tamil Nadu, India Proceedings of India International Conference on Power Electronics 2006

3. Automatic Brakes leads to Minimized Cost and Time

4. Membership Functions 15 meters/s = 33.5 miles/h 40 meters/s = 89.5 miles/h 500 meters = 0.31 miles or 5.46 football fields

5. Rule Surfaces

6. Results Using 1.3 m/s^2 Deceleration km/h

7. Speed vs Distance Brake Deceleration 1.3m/s^2

8. Braking system Train weight with engine Decce leration (g) m/s^2 Rails Train speed (mph) Stopping distance (yards) Time to stop (s) Steel & McInnes air197t 7cwt0.051 0.4998 Wet49.553434.5 Smith vacuum262t 7cwt0.057 0.5586 dry49.548329 Clark and Webb chain 241t 10cwt 0.056 0.5488 dry47.547929 Barker's hydraulic210t 2cwt0.056 0.5488 dry50.7551632 Westinghouse vacuum204t 3cwt0.052 0.5096 wet5257634.5 Fay mechanical186t 3cwt0.057 0.5586 wet44.538827.5 Clark hydraulic198t 3cwt0.075 0.735 dry5240422.75 Westinghouse automatic203t 4cwt0.099 0.9702 dry5230419 Emergency Brake (Without track brakes) 1.5 Magnetic Track Brake1.8

9. Different Speeds 100 m/s = 223.7 mph 60 m/s = 134.2 mph (fastest)

10. Modified Rule Surface slope = slopeFunction(s); center = 13 +.5 * s ^ 2; brakingSurface(sIndex, dIndex) = sigmoid(d, center, slope) * 100;

11. Modified Results

12. Modified Results Speed vs. Distance

13. Conclusion Fuzzy P.I.E. + Easier and Intuitive - Did not scale automatically with different speeds Modified controller + More accurate stopping from different speeds + Easier computation - Not intuitive and difficult to modify

14. Future Work Look into the grade of the track as an input