| The type-2 fuzzy logic controller is unique in its ability to handle the effects of different types of uncertainties. Nevertheless, the high computation cost of type-2 fuzzy logic controllers has practically hindered the application of these controllers to real-world problems. This is especially true in the rapidly growing field of mobile robotics where autonomous control has become a large area of interest and research. Key challenges facing real-world control involve dealing with the noise from a variety of sources inherent within mobile robotic platforms and environments, while simultaneously maintaining a low computational cost. To deal with these issues, this thesis develops a novel interval type-2 Takagi-Sugeno-Kang fuzzy logic controller which offers significant reduced computation compared to the standard type-2 fuzzy logic controllers. To further reduce the complexity of the controller, the theory of uncertainty bounds is incorporated into the output processing which significantly reduces computational complexity at the cost of a bounded error in output estimation. To demonstrate the effectiveness of the proposed controller in this thesis, the proposed controller is simulated and applied to the task of cart centering. It is shown that controller can handle noise and achieve its goal. In addition, to demonstrate the real-world effectiveness of the proposed controller, the proposed algorithm is implemented for the control of popular Turtlebot ground robots to achieve a simplified formation. |
