The main idea of fuzzy logic control (FLC) is to build a model of a human control expert who is capable of controlling the plant without thinking in terms of a mathematical model

2. Idea about fuzzy logic control ?
Why tuning of fuzzy logic controller is necessary?
Combination of neural network and fuzzy logic.

3.  Fuzzyfication
 Inferences
 Defuzzyfication

4. Architecture:-
Feed-forward
neural network

5. Four possible
cases:-
Cd located between
Mi & Mi+1.
Cd located between
Mi & Mi+1.
Cd not located
between Mi & Mi+1.
Cd not located
between Mi & Mi+1.

6. Step 1:- Ca along with desired value Cd.
Step 2:- Check whether Cd lies Between.
Step 3:-Check whether Cd > Ca or not.
Step 4:-Cd>Ca
Step 5:- Cd< Ca
Step 6:-now if Cd lies outside.
Step 7:-move back to 1.

8.  Online Training :-
 Microcontroller is used.
 Offline Training :-
 Either system taken off-line or able to model
/simulate accurately.
 Restriction:
 Training data available
 Can be modeled or simulated
 Knowledge base is available to designer.

9. ADAPTIVE NEUROFUZZY CONTROLLER :-

10. PROPORTIONAL NEUROFUZZY CONTROLLER :-

11. Second order type zero system.

13. No need to have mathematical model of system.
Possible to implement expert human knowledge and
experience.
Possible to control non-linear plant.
Possible to control fast processes.

14. Not possible to show stability of controlled
system.
Human knowledge is incomplete even if
operator is exist.
No standard and systematic method for
transferring human knowledge and experience.
Computing time could be long.

15. The khepera mobile robot.

16. Control of vacuum cleaner.
4 inputs –dust, Vacuum, cleaning
direction, surface type.
2 outputs-suction
power, brush cleaning
speed.

17. Innovative approach of hybrid neurofuzzy
controller is present.
Neural networks learning capability to fuzzy
logic controller.
Generic algorithm is for learning and training.