Industrial robotic systems with fuzzy logic controller and neural network

Generally, when we control the robot, we should calculate exact inverse kinematics. However, inverse kinematics calculation is complex and it takes much time for the manipulator to control in real time. Therefore, the calculation of inverse kinematics can result in a significant control delay in real time. We present a method in which inverse kinematics can be calculated through fuzzy logic mapping, based on an exact solution through fuzzy reasoning instead of inverse kinematics calculation. Also, the result provides sufficient precision and transient tracking error can be controlled based on a fuzzy adaptive scheme. We also demonstrate that neural networks can be used effectively for the control of a nonlinear dynamic system with uncertain or unknown dynamics models and applied to the control robot. The advantage of using the neural approach over the conventional inverse kinematics algorithms is that neural networks can avoid time consuming calculations. We represent a good control efficiency through simulation of a 2-DOF manipulator by fuzzy logic controller, and demonstrate the effectiveness of the proposed learning scheme using feedforward neural networks, too