Robot Control by Fuzzy Logic

Fuzzy set theory, originally developed by Lotfi Zadeh in the 1960’s, has become a popular tool for control applications in recent years (Zadeh, 1965). Fuzzy control has been used extensively in applications such as servomotor and process control. One of its main benefits is that it can incorporate a human being’s expert knowledge about how to control a system, without that a person need to have a mathematical description of the problem. Many robots in the literature have used fuzzy logic (Song & Tay, 1992), (Khatib, 1986), (Yan et al., 1994) etc. Computer simulations by Ishikawa feature a mobile robot that navigates using a planned path and fuzzy logic. Fuzzy logic is used to keep the robot on the path, except when the danger of collision arises. In this case, a fuzzy controller for obstacle avoidance takes over. Konolige, et al. use fuzzy control in conjunction with modeling and planning techniques to provide reactive guidance of their robot. Sonar is used by robot to construct a cellular map of its environment. Sugeno developed a fuzzy control system for a model car capable of driving inside a fenced-in track. Ultrasonic sensors mounted on a pivoting frame measured the car’s orientation and distance to the fences. Fuzzy rules were used to guide the car parallel to the fence and turn corners (Sugeno et al., 1989). The most known fuzzy models in the literature are Mamdani fuzzy model and TakagiSugeno-Kang (TSK) fuzzy model. The control strategy based on Mamdani model has the linguistic expression (Mamdani, 1981):