Abstract

Since fuzzy logic controllers (FLCs) can handle complex systems without knowing much about the systems' mathematical model, they are widely used for a range of robotic control applications. Further, the ability of FLCs (particularly, type-2 FLCs) to effectively capture and accommodate uncertainties has made them one of the suitable choices for implementing robotic control applications in uncertain environments. However, developing type-1 and type-2 FLCs for real-time robotic control applications is relatively more challenging than developing traditional controllers such as PID controllers. The reason is, the fuzzy logic calculations involved are more complex and not much tools have been developed to assist FLC application developers. In this paper, therefore, using an object-oriented approach and unified model language (UML), we demonstrate a systematic approach for developing a new generic and configurable fuzzy logic system (FLS) library that eases the implementation of real-time type-1 and interval type-2 FLC applications based on both Mamdani and Takagi-Sugeno-Kang (TSK) inference mechanisms. To evaluate the developed library, we have implemented it for the interval type-2 TSK fuzzy logic altitude control of a quadcopter unmanned aerial vehicle (UAV). The response of this fuzzy logic controller is then compared with the response of a classical PD controller.

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