Comparative Study of Takagi-Sugeno-Kang and Madani Algorithms in Type-1 and Interval Type-2 Fuzzy Control for Self-Balancing Wheelchairs

This study examines the effectiveness of four different fuzzy logic controllers in self-balancing wheelchairs. The controllers under consideration are Type-1 Takagi-Sugeno-Kang (TSK) FLC, Interval Type-2 TSK FLC, Type-1 Mamdani FLC, and Interval Type-2 Mamdani FLC. A MATLAB-based simulation environment serves for the evaluation, focusing on key performance indicators like percentage overshoot, rise time, settling time, and displacement. Two testing methodologies were designed to simulate both ideal conditions and real-world hardware limitations. The simulations reveal distinct advantages for each controller type. For example, Type-1 TSK excels in minimizing overshoot but requires higher force. Interval Type-2 TSK shows the quickest settling times but needs the most force. Type-1 Mamdani has the fastest rise time with the lowest force requirement but experiences a higher percentage of overshoot. Interval Type-2 Mamdani offers balanced performance across all metrics. When a 2.7 N control input cap is imposed, Type-2 controllers prove notably more efficient in minimizing overshoot. These results offer valuable insights for future design and real-world application of self-balancing wheelchairs. Further studies are recommended for the empirical testing and refinement of these controllers, especially since the initial findings were limited to four-wheeled self-balancing robotic wheelchairs

Comparative Performance of Mamdani and Sugeno Fuzzy Logic Control Systems in Governing the Motion of a Robotic Arm

In this research, a simulation study of a prototype medical robotics system was conducted to evaluate the performance of Mamdani and Sugeno fuzzy logic control systems in response to varying Step Input values. The Mamdani control system demonstrated faster response times and better adherence to setting time in the absence of disturbances. However, the Sugeno system outperformed Mamdani in scenarios where overshoot percentage was a critical factor. Even in the presence of disturbances, Mamdani maintained faster response times, lower Risetime, and minimal or no overshoot. Nevertheless, Mamdani's setting time responses were sometimes similar to or slower than Sugeno, which may be attributed to Mamdani's higher fuzziness compared to Sugeno's more linear nature. In conclusion, Mamdani exhibited superior speed and adherence to setting time when overshoot percentage was not a critical factor. Furthermore, Mamdani's higher fuzziness, compared to Sugeno's linearity, may explain the observed differences in responses between the two fuzzy logic control systems. &nbsp