1. Intelligent vs Classical Control Bax Smith EN9940

2. Today’s Topics Distinguishing Between Intelligent and Classical Control Methods of Classical Control Methods of Intelligent Control Applications for Both Types of Control Discussion

3. Distinguishing b/w Intelligent and Classical Control

4. Classical Control The Mathematicians Approach – Rigidly Modeled System Software does what it is told – Intelligence comes from the Designer

5. Intelligent Control The Lazymans Approach – System not Rigidly Modeled Software does what it wants to – Intelligence comes from the Software

6. Shifting Intelligence Software Designer Increasing Intelligence Designer Software Classical Control Intelligent Control

7. Methods for Classical Control

8. Open-Loop Control System

9. Closed-Loop Control System

10. System Modeling First-Order System: Second-Order System:

11. Classical Control Examples PID Control Optimal Control Discrete-Event Control Hybrid Control

12. PID Control Proportional Control – Pure gain adjustment acting on error signal Integral Control – Adjust accuracy of the system Derivative Control – Adjust damping of the system

13. PID Control

14. Optimal Control (LQR)

16. Inverted Pendulum

17. Inverted Pendulum Model

18. Methods for Intelligent Control

19. Intelligent Control Examples Fuzzy Logic Control Neural Network Control Genetic Programming Control Support Vector Machines Numerical Learning COMDPs - POMDPs

20. No System Modeling Software learns system model

21. Fuzzy Logic Control Multi-valued Logic – Rather warm/pretty cold vs hot/cold – Fairly dark/very light vs Black/White Apply a more human-like way of thinking in the programming of computers

22. Sets Set A = {set of young people} = [0,20] Is somebody on his 20th birthday young and right on the next day not young?

23. Fuzzy Sets

24. Fuzzy Example – Inverted Pendulum

25. Fuzzy Rules If angle is zero and angular velocity is zero then speed shall be zero If angle is zero and angular velocity is pos. low then speed shall be pos. low …

26. Actual Values

27. Neural Network Control Mimic Structure and Function of the Human Nervous System

28. Biological Neurons Dendrites – Connects neurons – Modify signals Synapses – Connects Dendrites Neuron – Emits a pulse if input exceeds a threshold – Stores info in weight patterns

29. Mathematical Representation of a Neuron

30. Back-Propagation Neural Network

31. Training a Neural Network Analogous to teaching a child to read – Present some letters and assign values to them – Don’t learn first time, must repeat training – Knowledge is stored by the connection weights Minimize the error of the output using LMS algorithm to modify connection weights

32. Genetic Programming Control Output of Genetic Programming is another computer program!

33. Genetic Programming Steps Generate a random group of functions and terminals (programs) – Functions: +, -, *, /, etc… – Terminals: velocity, acceleration, etc… Execute each program assigning fitness values Create a new population via: – Mutation – Crossover – Most fit Which ever program works best is the result

34. Crossover Operation

35. Mutation Operation

36. Applications In general, – Use Classical Control (Intelligent Control can take long to train) If problem too complex – Use Intelligent Control

37. Discussion