Knowledge-based Mechanical Arm Modeling of Bascule Lift with Simulation Method and Fuzzy Inference Approach

Thus far, controllers have often used the equations governing direct kinematics or reverse kinematics to control the position of the final implement of the robotic arm. Difficulties in unravelling direct kinematic equations and reverse kinematics, errors in solving equations, lack of user-friendly graphical environment, lack of flexibility in controller decision making and large volumes of calculations are problems of control systems in controlling robotic arms. In this paper, a skeletal lift robot with two classic PID and Fuzzy control methods with 4 degrees of freedom, in addition to a simulation in which four parts of the arm were examined by the controller and Matlab / Simulink as a tool for feature testing were used. Robotic arm movements were used. Implementation outputs show the satisfactory performance of the fuzzy controller which was able to control the arms of this robot with a percentage of uplift and a desired sitting time of 1.23 seconds. Furthermore, in order to test the performance of the scaffolding robot, the movement nodes of the arm were placed optimally and the controlled system showed the high accuracy of the fuzzy controller so that after 1.23 seconds at the critical point, the wrist area, it was able to continue with the desired sitting with a permanent error of zero and without any distortion.