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Multi-Agent Machine Learning
book

Multi-Agent Machine Learning

by H. M. Schwartz
August 2014
Intermediate to advanced content levelIntermediate to advanced
256 pages
6h 48m
English
Wiley
Content preview from Multi-Agent Machine Learning

5.6 Fuzzy Controller Structure

We use two inputs (fuzzy variables) to the fuzzy controller and generate one output from the fuzzy controller. The inputs for the pursuer are the angle difference c05-math-0238 and its rate of change c05-math-0239. The inputs for the evader are the angle difference c05-math-0240 and the distance c05-math-0241. We add the distance as an input to the fuzzy controller for the evader. The reason is that the evader has higher maneuverability than the pursuer and the distance between the evader and the pursuer is critical for the evader to decide if it needs to make a sharp turn.

For simplicity and to avoid the curse of dimensionality, we use two inputs and three fuzzy sets for each input to construct the controller. The pursuer's fuzzy sets are negative (N), zero (Z), and positive (P) for the angle difference c05-math-0242 and its derivative c05-math-0243. The evader's fuzzy sets are negative (N), zero (Z), and positive (P) for the angle, ...

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Publisher Resources

ISBN: 9781118362082Purchase book