جستجوی مقالات مرتبط با کلیدواژه "بازوی ربات" در نشریات گروه "فنی و مهندسی"

The science of robotics has been widely applied to develop industries and specifically to reduce human injuries. By adjusting the controller of these robots to achieve proper speed and accuracy, their performance can be improved which it can lead to reduction in the number of injuries. Until now, robotic arm controllers have often been modeled using equations of direct and inverse kinematics, aiming to control the position of the final actuator of the arm. Difficulty in solving these equations, errors in solving them, lack of user-friendly environment, inflexibility in decision-making and the volume of calculations are among the problems of existing robotic control systems. In this paper, the robotic arm with four degrees of freedom is controlled by two classic and Fuzzy control methods and three PI, PD and PID controllers. Matlab-Simulink is used as a tool to test the movement characteristics of the robot. It was observed that the PD controller, despite not having overshoot, leads to a steady state error in most of the cases. Also, the PI controller provides a proper settling time, while, it has a higher overshoot than the PID controller. Finally, the results showed that the Fuzzy PID controller provides better responses than the classical PID controller and the classical and Fuzzy PI and PD controllers.

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.

This paper presents a new approach to design a controller for nonlinear systems using the iterative approximation method. In the proposed approach, the nonlinear system is first approximated by several linear time-varying systems. Then, for each of the linear systems, a control law is designed and the calculated control law for the last system is applied to the nonlinear system. This paper shows that the conventional method in the iterative approximations is practically open-loop and becomes unstable after a short time. Then, the maximum duration of the stability in conventional controllers called T is evaluated using simulations in the case of a robot arm. In this paper, to establish a relationship between the controller and the nonlinear system, the process of approximation and design of the controller is repeated every T seconds. In this way, the control system is closed-loop, and every T second, using the updated feedback signal form the nonlinear system the control signal is updated. Finally, the efficiency of the proposed method is shown through a simulation.

Visual servoing system is a system to control a robot by visual feedback so that robot drives from any arbitrary start position to the target positions. Various ways، including control by using model of the robot، designing controller directly، and using Jacobian matrix have been studied. Since there is not access to model of robot and obtaining a model of robot would be difficult and time consuming، in many cases، the control law is obtained using Jacobian matrix. In this paper، inverse of Jacobian matrix is approximated using artificial neural networks. The approximated neural models are used in control law directly. For each degree of freedom of the robot manipulator، a two-layer feedforward neural network is considered. The distance between end-effector and target along the x-axis and y-axis، and the shoulder joint coordinates along the x-axis and y-axis are the inputs of each of the networks and the outputs are the fraction of the related robot joint changes to the image features changes (the elements of the inverse of Jacobian matrix). The proposed method has been implemented on a real robot manipulator. The experimental results show that the proposed control system can move the end-effector to different target positions in workspace with good accuracy.