• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.3
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• pp.57-64
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• 2005

In two-degree of freedom(TDOF) inertial sensors, two axes are mechanically correlated with each other. Fault source of one axis sensor may affect the other axis sensor, and therefore multiple fault detection and isolation(FDI) technique is required. Conventional FDI techniques using hardware redundancy need four TDOF inertial sensors for FDI. In this study, three TDOF inertial sensor redudancy case is considered, where conventional FDI technique can detect the fault, but cannot isolate the fault sensor. Hybrid FDI technique is proposed to solve this problem. Hybrid FDI technique utilizes the analytic redundancy by utilizing the unscented kalman filter as well as hardware redundancy for FDI. To verify the effectiveness of the proposed FDI technique, numerical simulations are performed using six degree of freedom nonlinear aircrft dynamics.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.2
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• pp.102-106
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• 2013

A cooperative navigation method for cooperative flight of UAVs is proposed. The commonly used navigation method for UAVs is based on GNSS measurements. However, when it is not available by jamming or other causes, an alternative method is needed. In this paper, it is shown that UAVs equipped with inertial sensors, passive sensor and wireless communication link can perform accurate navigation through sharing information with each other. Firstly, the appropriate roles for sensors and wireless communication link are assigned. Secondly, a filter to perform navigation cooperative is constructed. Finally, the boundedness of estimation error of the filter under small initial estimation error is analyzed. The simulation results show that the proposed method can reduce navigation errors effectively.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.3
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• pp.190-194
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• 2013

This paper presents experimental results of the attitude control for a two-rotor system with 3-DOF(degree-of-freedom). Two DC motors are equipped at the two ends of a rectangular beam to generate lift force and the relation between motor voltage and lift force is found experimentally. And inertial sensors are mounted at the center of the beam to measure the roll angle and a complementary filter is designed to get the angle during DC motors driving. A controller with nonlinear compensation, integrator and state feedback to achieve asymptotic tracking for a step input and reject input disturbance is designed and experimented.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.493-495
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• 2004

In this paper, shows a research in accordance with the design the implementation of the localization system for mobile robot using INS(Inertial Navigation System) and GPS(Global Positioning System). First, a Strapdown Inertial Navigation System : SDINS is designed and implemented for low speed walking robot, by modifying Inertial Navigation System which is widely used for rocket, airplane, ship and so on. In addition, thesis proposes the localization of robot with the method of loosely coupled method by using Kalman Filter with INS/GPS integrated system to utilize assumed position and steed data from GPS.

• Smart Structures and Systems
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• v.22 no.1
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• pp.95-103
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• 2018

Active vibration control via inertial mass actuators has been shown as an effective tool to significantly reduce human-induced vertical vibrations, allowing structures to satisfy vibration serviceability limits. However, a lot of practical obstacles have to be solved before experimental implementations. This has motivated simple control techniques, such as direct velocity feedback control (DVFC), which is implemented in practice by integrating the signal of an accelerometer with a band-pass filter working as a lossy integrator. This work provides practical guidelines for the tuning of DVFC considering the damping performance, inertial mass actuator limitations, such as stroke and force saturation, as well as the stability margins of the closed-loop system. Experimental results on a full scale steel-concrete composite structure (behaves similar to a footbridge) with adjustable span are reported to illustrate the main conclusions of this work.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.2
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• pp.140-145
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• 2013

This paper proposes an efficient integration method for GPS (Global Positioning System) and INS (Inertial Navigation System). To obtain accuracy and computational conveniency at the same time with low cost global positioning system receivers and micro mechanical inertial sensors, a new mechanization method and a new filter architecture are proposed. The proposed mechanization method simplifies velocity and attitude computation by eliminating the need to compute complex transport rate related to the locally-level frame which continuously changes due to unpredictable vehicle motions. The proposed filter architecture adopts two heterogeneous filters, i.e. position-domain Hatch filter and velocity-aided Kalman filter. Due to distict characteristics of the two filters and the distribution of computation into the two hetegrogeneous filters, it eliminates the cascaded filter problem of the conventional loosly-coupled integration method and mitigates the computational burden of the conventional tightly-coupled integration method. An experiment result with field-collected measurements verifies the feasibility of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.2
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• pp.174-185
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• 2006

This paper describes a DR construction for land navigation and the sigma point based receding horizon Kalman FIR (SPRHKF) filter for DR/GPS hybrid navigation system. A simple DR construction is adopted to improve the performance both of the pure DR navigation and the DR/GSP hybrid navigation system. In order to overcome the flaws of the EKF, the SPKF is merged with the receding horizon strategy. This filter has several advantages over the EKF, the SPKF, and the RHKF filter. The advantages include the robustness to the system model uncertainty, the initial estimation error, temporary unknown bias, and etc. The computational burden is reduced. Especially, the proposed filter works well even in the case of exiting the unmodeled random walk of the inertial sensors, which can be occurred in the MEMS inertial sensors by temperature variation. Therefore, the SPRHKF filter can provide the navigation information with good quality in the DR/GPS hybrid navigation system for land navigation seamlessly.

• Journal of Sensor Science and Technology
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• v.25 no.3
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• pp.202-207
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• 2016

Estimation of vertical position is critical in applications of sports science and fall detection and also controls of unmanned aerial vehicles and motor boats. Due to low accuracy of GPS(global positioning system) in the vertical direction, the integration of IMU(inertial measurement unit) with the GPS is not suitable for the vertical position estimation. This paper investigates an IMU-barometer integration for estimation of vertical position (as well as vertical velocity). In particular, a new two-step Kalman/complementary filter is proposed for accurate and efficient estimation using 6-axis IMU and barometer signals. The two-step filter is composed of (i) a Kalman filter that estimates vertical acceleration via tilt orientation of the sensor using the IMU signals and (ii) a complementary filter that estimates vertical position using the barometer signal and the vertical acceleration from the first step. The estimation performance was evaluated against a reference optical motion capture system. In the experimental results, the averaged estimation error of the proposed method was 19.7 cm while that of the raw barometer signal was 43.4 cm.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.4
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• pp.363-363
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• 2001

We study on the velocity matching algorithm for transfer alignment of inertial navigation system(INS) using a robust H₂ filter. We suggest an uncertainty model and a discrete robust H₂filter for INS and apply the suggested robust H₂ filter to the uncertainty model. The discrete robust H₂filter is shown by simulation to have better performance time and accuracy than Kalman filter.

• The Journal of Korea Robotics Society
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• v.15 no.1
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• pp.16-23
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• 2020

This paper describes an alignment algorithm that estimates the initial heading angle of AUVs (Autonomous Underwater Vehicle) for starting navigation in a sea area. In the basic dead reckoning system, the initial orientation of the vehicle is very important. In particular, the initial heading value is an essential factor in determining the performance of the entire navigation system. However, the heading angle of AUVs cannot be measured accurately because the DCS (Digital Compass) corrupted by surrounding magnetic field in pointing true north direction of the absolute global coordinate system (not the same to magnetic north direction). Therefore, we constructed an experimental constraint and designed an algorithm based on extended Kalman filter using only inertial navigation sensors and a GPS (Global Positioning System) receiver basically. The value of sensor covariance was selected by comparing the navigation results with the reference data. The proposed filter estimates the initial heading angle of AUVs for navigation in a sea area and reflects sampling characteristics of each sensor. Finally, we verify the performance of the filter through experiments.