1. Self-training Neural Net
for
Process control
Ken Stanton
Clint Perdue

2. Outline
 Design procedure – baby steps
 Pseudo code
 Fuzzy rules
 Simulink / matlab interaction
 Engine model
• System states
 Driving schedule
 NNet size and setup
 Compare to original
• RPM fall ringing, overrev, stall
 Calculations of original gains vs. final gains (avg)
 80% of time in MATLAB design
 Show non-linearity – throttle
 Engine load constant?
 Relationship to original presentation – diagrams, theory, approach, etc.

3. Fuzzy tuning rule set.
∆ Kp
∆ Ki
∆ Kd
+
0
-
+
0
-
+
0
-
Limit
0 t
Limit
0 t
Limit
0 20 0 ∆ ref 0 10 0 2
Trise Tsetl %OV ZCP
+ ZCN
MFE ZCP
- ZCN
ZCP+ZCN>1
Else

4. Fuzzy tuning - processing.
∆ Kp
∆ Ki
∆ Kd
+
0
-
+
0
-
+
0
-
Limit
0 t
Limit
0 t
Limit
0 20 0 ∆ ref 0 10 0 2
Trise Tsetl %OV ZCP
+ ZCN
MFE ZCP
- ZCN
ZCP+ZCN>1
Else
Σ
Σ
Σ

5. Phase 1, suggest new gains
from fuzzy rules’ evaluation of
performance.
Input PID Plant
gains(t+1)
Fuzzy
observer

6. Test of fuzzy tuning rules
against a second order system.

7. Phase 2, train N-Net to predict
proper gains.
Input PID Plant
gains (t+1)
Fuzzy
observer
N-Net
Control
gains (t)

8. Engine Model

9. Engine Model - Controller

10. Engine Model – Non-linearities

11. Initial System Responses

12. Early Controller

13. City Driving Schedule
City Driving Schedule
0
10
20
30
40
50
60
1 53 105 157 209 261 313 365 417 469 521 573 625 677 729 781 833 885 937 989 1041 1093 1145 1197 1249 1301 1353
Time (sec)
VehicleSpeed(mph)

14. City Driving Baseline Test

15. City Driving Baseline – Close Up
1

16. City Driving Baseline – Close up
2

17. Training Progress Illustration –
First exposure to system

18. Training Progress – Close up 1

19. Training Progress – Close up 2

20. Simulation with trained NNet

21. Simulation with trained NNet –
Close up 1

22. Simulation with trained NNet –
Close up 2

23. Trained Neural Net – PID maps
P gains

24. Trained Neural Net – PID maps
I gains

25. Trained Neural Net – PID maps
D gains