Inverse kinematics solution for trajectory tracking using artificial neural networks for SCORBOT ER-4u

This paper presents the kinematic analysis of the SCORBOT-ER 4u robot arm using a Multi-Layered Feed-Forward (MLFF) Neural Network. The SCORBOT-ER 4u is a 5-DOF vertical articulated educational robot with revolute joints. The Denavit-Hartenberg and Geometrical methods are the forward kinematic algorithms used to generate data and train the neural network. The learning of forward-inverse mapping enables the inverse kinematic solution to be found. The algorithm is tested on hardware (SCORBOT-ER 4u) and reliable results are obtained. The modeling and simulations are done using MATLAB 8.0 software

Trajectory tracking of a quadrotor with disturbance rejection using extended state observer

The presence of external disturbances such as wind may affect the stability of a quadrotor during flight. This paper proposes a robust autonomous flight control of a feedback linearized quadrotor model with the presence of external disturbances by using the active anti-disturbance control (AADC) technique. In the inner-loop control, the feedback linearization technique is used to simplify the nonlinear and under-actuated quadrotor dynamics into the corresponding linear representation. In the outer-loop control, the AADC technique using extended state observer (ESO) and state feedback is proposed for trajectory tracking and disturbance rejection control of the quadrotor, respectively. Here, ESO estimates the external disturbances by using only the output of the system. To evaluate the effectiveness of the proposed controller, simulations of the quadrotor were carried in which the results obtained show the advantage of the proposed control algorithm for hovering and trajectory tracking of the quadrotor