• Journal of Institute of Control, Robotics and Systems
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• v.14 no.6
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• pp.595-602
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• 2008

In this paper, performance of the initial alignment for INS whose attitude is not leveled is investigated. Observability of the initial alignment filter is analyzed and estimation errors of the estimated state variables are derived. First, the observability is analyzed using the rank test of observability matrix and the normalized error covariance of the Kalman filter based on the 10-state model. In result, it can be seen that the accelerometer biases on horizontal axes are unobservable. Second, the steady-state estimation errors of the state variables are derived using the observability equation. It is verified that the estimates of the state variables have errors due to the unobservable state variables and the non-leveling tilt angles of a vehicle containing the INS. Especially, this paper shows that the larger the tilt angles of the vehicle are, the larger the estimation errors corresponding to the sensor biases are. Finally, it is shown that the performance of the 8-state model excepting the accelerometer biases on horizontal axes is better than that of the 10-state model in the initial alignment by simulation.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.2
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• pp.265-272
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• 1996

In this paper, attitude computation algorithm for a strap down ARS(Attitude Reference System)of an underwater vehicle has been studied. Attitude errors o the ARS using low-level gyroscopes tend to increase with time due to gyroscope errors. To cope with this problem, a mixing algorithm of accelerometer aided attitude computation has been developed. The algorithm can successfully bound the error increase for cruising motion, but it gives instantaneously large errors when a vehicle maneuvers. To improve the performance in case of vehicle's maneuver, a new attitude computation mixing algorithm complying state of vehicle and to manage the adjustment of the gains which are invariant in the existing algorithm. In addition, a gain scheduling method is applied to fuzzy inference composition process for real-time computation. Monte Carlo simulation results show that the proposed algorithm provides better performance than the existing algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.5
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• pp.839-844
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• 2008

A novel state of health estimation method for hybrid electric vehicle lithium battery using sliding mode observer has been presented. A simple R-C circuit method has been used for the lithium battery modeling for the reduced calculation time and system resources due to the simple matrix operations. The modeling errors of simple model are compensated by the sliding mode observer. The design methodology for state of health estimation using dual sliding mode observer has been presented in step by step. The structure of the proposed system is simple and easy to implement, but it shows robust control property against modeling errors and temperature variations. The convergence of proposed observer system has been proved by the Lyapunov inequality equation and the performance of system has been verified by the sequence of urban dynamometer driving schedule test. The test results show the proposed observer system has superior tracking performance with reduced calculation time under the real driving environments.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.7
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• pp.702-710
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• 2008

In this paper, the observability of the INS/GPS integrated system for a land vehicle is analyzed on measurements and different filters with respect to the measurements are designed. In the stationary case, it is shown that horizontal accelerometer biases and vertical attitude errors and gyro biases are unobservable. An 8-state filter is designed based on the observability analysis. When GPS signal is available, a 15-state filter is used with position and velocity measurements. To estimate the INS errors even in the case that GPS signal is blocked a filter is designed in consideration of the non-holonomic constraints of a land vehicle. In this case, the horizontal position and velocity errors and vertical attitude error are unobservable. However, a 12-state filter including the velocity states is designed to estimate the accelerometer biases. When GPS signal recovers, a 9-state filter is used excluding the sensor biases. This paper presents a multi-dimensional filter that switches the four filters according to the usable measurements and maneuver environments. A simulation is carried out to verify the performance of the proposed filter.

• Korean Journal of Computational Design and Engineering
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• v.19 no.4
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• pp.340-346
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• 2014

There are many factors that contribute to hit probability of the gun shot of ground combat vehicles. Aiming accuracy is mainly affected by the dynamic state of the vehicle. The stabilization error of the turret under system vibration is one of the major factors that affect the aiming accuracy. The vibration of the vehicle is affected by both the state of the road and the speed of the vehicle. This paper analyzes the aiming accuracy of the gun equipped on the GCV when the vehicle drives on the different roads and at different speed. The vertical displacement and the pitch angle of the gun are calculated and the impact points of the target are calculated. Distribution of the impact points on the target is greatly influenced by the pitch rotation rather than vertical displacement. And this aiming errors result in the errors of point of impacts on the target after the bullet flies through the air under trajectory equations. The GCV is modeled using a half-car model with 6 D.O.F. and the specifications of the M2 machine gun are used in trajectory calculation simulation and the target is located in 1000 m away from the gun.

• ETRI Journal
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• v.38 no.3
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• pp.579-588
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• 2016

To improve the ability to determine a vehicle's movement information even in a challenging environment, a hybrid approach called non-Gaussian square rootunscented particle filtering (nGSR-UPF) is presented. This approach combines a square root-unscented Kalman filter (SR-UKF) and a particle filter (PF) to determinate the vehicle state where measurement noises are taken as a finite Gaussian kernel mixture and are approximated using a sparse Gaussian kernel density estimation method. During an outage-free GPS period, the updated mean and covariance, computed using SR-UKF, are estimated based on a GPS observation update. During a complete GPS outage, nGSR-UPF operates in prediction mode. Indeed, because the inertial sensors used suffer from a large drift in this case, SR-UKF-based importance density is then responsible for shifting the weighted particles toward the high-likelihood regions to improve the accuracy of the vehicle state. The proposed method is compared with some existing estimation methods and the experiment results prove that nGSR-UPF is the most accurate during both outage-free and complete-outage GPS periods.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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• pp.141-148
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• 2003

This paper presents a rotating ann test for assessment of an underwater hybrid navigation system for a semi-autonomous underwater vehicle. The navigation system consists of an inertial measurement unit (IMU), an ultra-short baseline (USBL) acoustic navigation sensor and a doppler velocity log (DVL) accompanying a magnetic compass. The errors of inertial measurement units increase with time due to the bias errors of gyros and accelerometers. A navigational system model is derived to include the error model of the USBL acoustic navigation sensor and the scale effect and bias errors of the DVL, of which the state equation composed of the navigation states and sensor parameters is 25 in the order. The conventional extended Kalman filter was used to propagate the error covariance, update the measurement errors and correct the state equation when the measurements are available. The rotating ann tests are conducted in the Ocean Engineering Basin of KRISO, KORDI to generate circular motion in laboratory, where the USBL system was absent in the basin. The hybrid underwater navigation system shows good tracking performance against the circular planar motion. Additionally this paper checked the effects of the sampling ratio of the navigation system and the possibility of the dead reckoning with the DVL and the magnetic compass to estimate the position of the vehicle.

• The Transactions of the Korean Institute of Power Electronics
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• v.12 no.4
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• pp.324-331
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• 2007

This paper studies new estimation method for state of charge (SOC) of the hybrid electric vehicle lithium battery using sliding mode observer. A simple R-C Lithium battery modeling technique is established and the errors caused by simple modeling was compensated by the sliding mode observer. The structure of the sliding mode observer is simple, but it shows robust control property against modeling errors and uncertainties. The performance of the system has been verified by the UUDS test. The test results of the proposed observer system shows robust tracking performance under real driving environments.

• Journal of Ocean Engineering and Technology
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• v.22 no.4
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• pp.106-113
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• 2008

This paper presents an underwater navigation system based on range measurements from a known reference station fixed on the sea bottom or floated at surface with a buoy, for which the system is extended to 3-dimensional coordinates. We formulated a state equation in polar coordinates and constituted an extended Kalman filter for discrete-time implementation of the navigation algorithm. The autonomous underwater vehicle, lSiMl, cruising with a constant speed can estimate its trajectory using just range measurements and additional depth, heading and pitch sensors. Simulation studies were performed to evaluate the underwater navigation of the maneuvering AUV with range measurements. We modulated the sample rate of range measurements to evaluate the effect of the update rate, and changed the initial position error of the AUV to check the robustness to estimation errors. Simulation results illustrates that the extended navigation system provides convergence of the state estimates. The navigation system was conditionally stable when it had initial position errors.

• Advances in nano research
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• v.13 no.6
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• pp.587-597
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• 2022

In the present study, a novel optimization method in formation control of multi -system vehicles based on the trajectory of the nearest neighbor trajectory is presented. In this regard, the state equations of each vehicle and multisystem is derived and the optimization scheme based on minimizing the differences between actual positions and desired positions of the vehicles are conducted. This formation control is a position-based decentralized model. The trajectory of the nearest neighbor are optimized based on the current position and state of the vehicle. This approach aids the whole multi-agent system to be optimized on their trajectory. Furthermore, to overcome the cumulative errors and maintain stability in the network a semi-centralized scheme is designed for the purpose of checking vehicle position to its predefined trajectory. The model is implemented in Matlab software and the results for different initial state and different trajectory definition are presented. In addition, to avoid collision avoidance and maintain the distances between vehicles agents at a predefined desired distances. In this regard, a neural fuzzy network is defined to be utilized in conjunction with the control system to avoid collision between vehicles. The outcome reveals that the model has acceptable stability and accuracy.