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In this paper, the trajectory tracking problem for a wheeled mobile robot in the presence of kinematic and dynamic uncertainties is considered. A nonlinear control approach based on lyapunove stability theory is proposed for this problem. Uncertainties are modeled as lumped disturbances and are estimated by a generalized disturbance observer using Linear Matrix Inequalities(LMI). These estimates are used for control laws design for compensating disturbances. Simulation results show the effectiveness of the proposed method in the presence of uncertainties generated by sliding velocity.

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The dynamic equations of an autonomous underwater vehicle (AUV) are described as a nonlinear system with multiple hydrodynamic coefficients which strongly affect the performance, maneuverability and controllability of AUV. On the other hand, the values of these coefficients depend on the vehicle speed and the geometric properties. In this paper, the nonlinear model identification problem of NPS AUV II, as a six degree-of-freedom (DOF) autonomous underwater vehicle, is addressed by using the nonlinear continuous-time extended Kalman filter (EKF) observer with guaranteed convergence. To this end, the hydrodynamic coefficients of AUV are considered as the augmented state variables of a six DOF nonlinear model. Based on the input-output data at the presence of the measurement noise of sensors, the state variables and the hydrodynamic coefficients of the nonlinear model in a (path) helical maneuver, are suitably estimated by using the EKF observer. In order to analyze the numerical performance of the proposed method, the dynamic equations of the vehicle are introduced, and a comparison is made between the identified model outputs and those of the real model.

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In recent years, simultaneous localization and mapping (SLAM) has become a very challenging matter as a basic problem in the mobile robots navigation. This paper describes a new efficient inertial SLAM algorithm for a UAV or an airborne. This inertialSLAM could be properly applied to two different kinds of sensors: (i)Range/Bearing sensorsand (ii)Bearing-only sensors and so it does not need to any other external positioning systems like as GPS or any preprovided data. In this study, acomprehensive system has been presented which not only enhances the three-dimensional SLAM accuracy and performance, but overcomes the two fundamental problems that have less been noticed in previous researchesA) To consider all the degrees of freedom for the UAV that lets the UAV height changesbe usedin addition to two x and y directions. B) It does not experience any limit for the symbols status which means that the system is able to observe all the symbolsin different heights based on inertia sensors. Finally, the accuracy of proposed algorithm has been approved due to the simulation results using the actual aircraft flight data.

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The forward kinematic problem of parallel robots is always considered as a challenge in the field of parallel robots due to the obtained nonlinear system of equations. In this paper, the forward kinematic problem of planar parallel robots in their workspace is investigated using a neural network based approach. In order to increase the accuracy of this method, the workspace of the parallel robot is divided into a number of smaller subspaces using the classifier and the boundary overlap method. After estimating the corresponding subspace, two separate neural networks are used in each subspace to determine the position and orientation of the moving platform. This approach is implemented on a 3-PRR planar parallel robot and its results are compared with the results obtained from the MLP, WaveNet, GMDH, Dual and Independent neural networks. Moreover, in order to evaluate the efficiency of the proposed method, a circular motion path is simulated using this approach and its performance is compared with the five mentioned methods. The results obtained from the implementation of this approach and comparison with the conventional methods indicates that the proposed method analyzes the forward kinematic problem of planar parallel robot with proper accuracy.