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Path Tracking for Robotic Manipulators

Control strategies used for robot manipulators can be classified in two types including “joint coordinates” and “Cartesian coordinates”. While in the ‘‘joint coordinates’’ approach the desired joint positions are specified, in the ‘‘Cartesian coordinate’’ approach the desired end effector position or path is specified in Cartesian space. The ‘‘Cartesian coordinate’’ approach are considered as one of the challenging problems encountered in robotic field. In this approach, robot manipulators are needed to track a predefined path in Cartesian space or reach to desired position in such a way that no collision with obstacles in the environment occurs. Therefore, feasible paths for each joint of robot must be determined in such a way that required criteria are met.


To perform Cartesian control of robot manipulators the following stages are needed:


1) Robot Environment Formation, which detects and computes the position of objects such as obstacles in robots environments


2) Path Planning, which generates path or velocity of each joints of robot, considering desired path or position of end-effector, obstacles and singularity    avoidance.


    3) Path Tracking, which enables the robot to follow the generated path and trajectory of joints.



In this project, different methods of path tracking for robotic manipulators are performed and compared. These methods enables robot manipulator to track determined paths for joints.Concerns of this stage are uncertainty in the parameters of robot which demands robust and adaptive approaches. 

Designed controllers are as follow:

    1) PD and PID as linear controllers

    2) Computed Torque and Passivity-Based Control as model based controllers

    3) Robust Computed Torque, Robust Passivity-Based Control, and Sliding Mode Control as robust controllers

    4) Adaptive Computed Torque, Adaptive Passivity-Based Control, and Nonlinear Model Reference Control as adaptive controllers

    5) Fuzzy PD, Fuzzy PID, Fuzzy Decision Table, and Fuzzy Sliding Mode Control as intelligent controllers