Adaptive P Control and Adaptive Fuzzy Logic Controller with Expert System Implementation for Robotic Manipulator Application

This study aims to develop an expert system implementation of P controller and fuzzy logic controller to address issues related to improper control input estimation, which can arise from incorrect gain values or unsuitable rule-based designs. The research focuses on improving the control input adaptation by using an expert system to resolve the adjustment issues of the P controller and fuzzy logic controller. The methodology involves designing an expert system that captures error signals within the system and adjusts the gain to enhance the control input estimation from the main controller. In this study, the P controller and fuzzy logic controller were regulated, and the system was tested using step input signals with small values and larger than the saturation limit defined in the design. The PID controller used CHR tuning to least overshoot, determining the system's gain. The tests were conducted using different step input values and saturation limits, providing a comprehensive analysis of the controller's performance. The results demonstrated that the adaptive fuzzy logic controller performed well in terms of %OS and settling time values in system control, followed by the fuzzy logic controller, adaptive P controller, and P controller. The adaptive P controller showed similar control capabilities during input saturation, as long as it did not exceed 100% of the designed rule base. The study emphasizes the importance of incorporating expert systems into control input estimation in the main controller to enhance the system efficiency compared to the original system, and further improvements can be achieved if the main processing system already possesses adequate control ability. This research contributes to the development of more intelligent control systems by integrating expert systems with P controllers and fuzzy logic controllers, addressing the limitations of traditional control systems and improving their overall performance

Application of PID Control System in Mecanum Wheelchair

This research centers on the design and implementation of a control system for an electric wheelchair equipped with Mecanum wheels. The study details a comprehensive research methodology starting with the creation of a block diagram to guide system design, hardware selection, and overall implementation. The electric wheelchair system incorporates power resources, input devices, and energy output mechanisms, utilizing a 24 VDC battery and a joystick with a 10K ohm potentiometer connected to an Arduino Due microcontroller. The operational workflow of the system is defined, enabling the wheelchair to respond to joystick commands for forward, left turn, right turn, and other movements. A PID control system is employed to regulate motor movement, enhancing control precision. The Cohen-Coon tuning method is used to determine the PID controller's gain, ensuring efficient closed-loop control. Results from PID controller experiments under P control and PD control are presented, demonstrating the system's responses for different gain values. Optimal performance is observed with a Kp value of 80 and Kd value of 1.2, showcasing improved response speed, reduced rise time, enhanced setting time, and lower percent overshoot. In conclusion, the combined proportional and derivative control system, specifically with Kp = 80 and Kd = 1.2, proves to be effective in enhancing the Mecanum wheelchair's performance. This study provides valuable insights into precise parameter adjustments for optimal control in Mecanum wheelchair applications

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