Earticle

능동형 질량 댐퍼는 AMD의 질량에 의해 유발되는 관성력을 이용하여 제어력을 생성하는 진동 제어 시스템의 일종이다. 제어력을 생성시키는 방법에 따라, 구조물의 응답 또는 변위를 액츄에이터의 입력신호로 사용하게 된다. 가속도 신호로부터 변위 또는 속도를 구하기 위해서는 적분과정이 필요하다. 본 논문에서는 AMD의 이송거리를 설계하기 위하여, 칼만 필터와 적분 필터가 안정한 알고리즘으로 소개된다. 수치 시뮬레이션 결과에서 두 필터는 정확하게 가속도 응답으로부터 상태변수를 추정할 수 있으며, AMD의 이송거리를 설계하기 위한 매우 강력한 알고리즘으로 사용될 것으로 사료된다.

An active mass damper is one of vibration control systems generating control forces using an inertia force induced by the mass of AMD. Depending on the control force generating method, the types of input signals to the actuator can be displacement or velocity of a structures. It is needed an integration process obtaining the state variable-displacement or velocity- from the acceleration signal. For the design of AMD’s stroke, in this study, the Kalman filter and integration filter are introduced which are known as stabilizing algorithm in the integration process. From the numerical simulation results, it is found that the filters can precisely estimate the state variable from acceleration responses, those are able to be used very powerful algorithm for the application of the AMD’s stroke design.

For onboard single-station passive direction-finding and location, if there is any abnormal error in the observation data, the extended Kalman filter (EKF) algorithm adopted thereby will cause inaccurate location result. In order to improve algorithm robustness, the robust equivalent gain matrix is constructed according to the standardized prediction residual error and the robust EKF algorithm is applied to the onboard single-station passive direction-finding and location. In allusion to the low efficiency of the robust EKF algorithm, the single-station passive location algorithm based on the improved extended Kalman filter is proposed in this article on the basis of combining F distribution statistic, and meanwhile single abnormal error and continuous abnormal error are added in the observation value to test the algorithm resistance to different abnormal errors. The simulation shows that the algorithm proposed in this article can well weaken the influence of abnormal errors on position estimation and the algorithm based on F distribution discriminant can improve location efficiency.

This paper presents a novel lateral stability control system for electric vehicle based on sideslip angle estimation through Kalman filter using the integration of a single antenna GPS receiver and yaw rate sensor. Using multi-rate measurements including yaw rate and course angle, time-varying parameters disappear from the measurement equation of the proposed Kalman filter. Accurate sideslip angle estimation is achieved by treating the combination of model uncertainties and external disturbances as extended states. Active front steering and direct yaw moment are integrated to manipulate sideslip angle and yaw rate of the vehicle. Instead of decoupling control design method, a new control scheme, "two-input two-output controller", is proposed. The extended states are utilized for disturbance rejection that improves the robustness of lateral stability control system. The effectiveness of the proposed methods is verified by computer simulations and experiments.

An integration system with multi-measurement sets can be realized via combined application of a centralized and federated Kalman filter. It is difficult for the centralized Kalman filter to remove a failed sensor in comparison with the federated Kalman filter. All varieties of Kalman filters monitor innovation sequence (residual) for detection and isolation of a failed sensor. The innovation sequence, which is selected as an indicator of real time estimation error plays an important role in adaptive mechanism design. In this study, the centralized Kalman filter with adaptive measurement fusion is introduced by means of innovation sequence. The objectives of adaptive measurement fusion are automatic isolation and recovery of some sensor failures as well as inherent monitoring capability. The proposed adaptive filter is applied to the GPS/SDINS integration system with an additional sensor. Simulation studies attest that the proposed adaptive scheme is effective for isolation and recovery of immediate sensor failures.

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.

GaAs on Si grown via metalorganic chemical vapor deposition is demonstrated using various Si substrate thicknesses and three types of dislocation filter layers (DFLs). The bowing was used to measure wafer-scale characteristics. The surface morphology and electron channeling contrast imaging (ECCI) were used to analyze the material quality of GaAs films. Only 3-㎛ bowing was observed using the 725-㎛-thick Si substrate. The bowing shows similar levels among the samples with DFLs, indicating that the Si substrate thickness mostly determines the bowing. According to the surface morphology and ECCI results, the compressive strained indium gallium arsenide/GaAs DFLs show an atomically flat surface with a root mean square value of 1.288 nm and minimum threading dislocation density (TDD) value of 2.4×10⁷ cm^-2. For lattice-matched DFLs, the indium gallium phosphide/GaAs DFLs are more effective in reducing the TDD than aluminum gallium arsenide/GaAs DFLs. Finally, we found that the strained DFLs can block propagate TDD effectively. The strained DFLs on the 725-㎛-thick Si substrate can be used for the large-scale integration of GaAs on Si with less bowing and low TDD.

This paper describes a GPS/DR integration filter for a car navigation system. A new GPS/DR integration filter is derived for obtaining more accurate and reliable position data. The covariance analysis results and simulation results are shown for evaluating the performance of the proposed GPS/DR integration filter.

In land navigation applications, two kinds of GPS/DR integration schemes are commonly used; the loosely-coupled integration scheme and the tightly-coupled one. The loosely-coupled integration filter has a simple structure and is easy to implement. When the number of visible satellites is insufficient, however, it cannot calibrate the errors of the DR sensors. On the contrary the tigthly-coupled integration filter can sup-press the growth of the error in the DR output even when the visibility is poor. However, it has larger com-putation load due to the state dimension and is inconsistent because of the variation in the measurement dimension. This paper presents a GPS/DR integration scheme with dual integration mode. During when the number of visible satellites is sufficient, the proposed scheme operates in a loosely-coupled integration mode. When the visibility becomes poor, it is switched into a tightly-coupled integration mode. Consequently, the pro-posed scheme can calibrate the DR sensors even when the visibility is poor. In addition, its computation time remains constant even if the number of visible satellites increases. Field experiment results show that the performance of the proposed integration method is almost similar to that of the tightly-coupled one.

In order to improve the performance of a GPS/DR integration system, the error sources of DR sensors should be modeled accurately, This results in the increases in the dimension of the integration filter and, consequently, computational load becomes large. To reduce the computational load, suggested in this paper is a decoupled GPS/DR integration scheme that consists of two cascaded Kalman filters. The GPS velocity output is used in the first filter to calibrate the DR sensor and to fix the velocity as well. The velocity from the first filter is fed to the second filter where the position is corrected using the GPS position output. Experimental results show that the proposed integration scheme has positioning performance comparable to the conventional coupled one, while its computation is reduced to about 2/3.

본 논문에서는 차량합법시스템에서 위치정확도 향상을 위한 방법으로 GPS/DR 통합시스템을 사용하여 DLM(Dynamic Linear Model) 알고리즘을 적용한 개선된 통합필터를 제안하였다. GPS/DR 통합 시스템의 성능을 개선하기 위해서는 DR 센서의 오류요소를 정확히 모델링 하여야 한다. GPS 로부터 위치를 계산하기 위해서는 적어도 3개 이상의 가시위성이 필요하다. 그러나 도심지에서는 고층빌딩이나 가로수 등에 의한 장애물로 인해서 3개 이상의 가시위성을 확보하기가 힘든 경우가 많다. 본 논문에서는 가시위성의 확보가 힘든 고층 빌딩이나 가로수길 등에서도 우수한 성능을 보이는 GPS 의사거리 및 의사거리율 정보를 이용하는 CPS/DR 통합 DLM 필터를 이용하여 성능을 개선시키는 방법을 제시한다.

This paper deals with single-phase unified power quality conditioner(UPQC), which aims at the integration of series-active and shunt-active filter. The series filter is used to compensate for the voltage distortions and the shunt filter is used to provide reactive power and counteract the harmonic current injected by the load. Also, the voltage of the DC link capacitor is controlled to a desired value by the shunt active filter. The performance of UPQC under load nonlinearities conditions is investigated using simulation as well as experimental results.