This article aims to compensate the velocity and position errors that exist when the star sensor starts to work in a strapdown inertial navigation system aided by celestial navigation. These systems are integrated via unscented Kalman filter to estimate the current attitude and the gyros fixed bias, precisely. Since an accurate integration is desired, the nonlinear attitude equations are utilized in filter and these equations are propagated through a precise discretization method. Then, implementing the back-propagation and smoothing techniques, the initial attitude and the accelerometers fixed bias are also estimated. Finally, carrying out a parallel navigation, the velocity and position errors are compensated. The validity of the proposed method is investigated through simulation of launch vehicle navigation. Simulation results show a great reduction in velocity and position errors.
This article aims to identify the roll channel parameters of an autonomous underwater vehicle. These parameters include hydrodynamic coefficients, motor torque, eccentricity, misalignments and mounting imperfections. In the proposed method, an approximation of the hydrodynamic coefficients is made at first via semi-empirical methods. In the next step, a proportional–integral–derivative controller is designed with respect to the approximated coefficients. Since the approximations can be very uncertain, the robustness of stability and performance of the proportional–integral–derivative controller is evaluated throughout µ-analysis. Finally, the unknown parameters are identified using the recorded data of on-board sensors during motion of the vehicle. The identification is based on minimization of the one-step prediction error. The minimization problem is nonlinear in unknown parameters, and particle swarm optimization is used to find an optimal solution. The performance of the proposed method is exhibited through a 6-degrees-of-freedom simulation of an autonomous underwater vehicle.
The purpose of this article is to develop an online method to identify the hydrodynamic coefficients of pitch plane of an autonomous underwater vehicle. To obtain necessary data for the identification, the dive plane dynamics should be excited through diving maneuvers. Hence, a controller is needed whose performance and stability are appropriate. To design such a controller, first, hydrodynamic coefficients are approximated using semi-empirical methods. Based on these approximated coefficients, a classic controller is designed at the next step. Since the estimation of these coefficients is uncertain, µ-analysis is employed to verify the robustness of stability and performance of the controller. Using the verified robust controller, some oscillating maneuvers are carried out that excite the dive plane dynamics. Using sensor fusion and unscented Kalman filter, smooth and high-rate data of depth is provided for the depth controller. A recursive identification algorithm is developed to identify the hydrodynamic coefficients of heave and pitch motions. It turns out that some inputs required by the identification are not measured directly by the sensors. But the devised fusion algorithm is able to provide the necessary data for identification. Finally, using the identified coefficients and employing pole placement method, a new controller with better performance is synthesized online. To evaluate the performance of the identification and fusion algorithms, a 6-degree-of-freedom simulation of an autonomous underwater vehicle is carried out.
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