• 제어로봇시스템학회논문지
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• 제11권6호
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• pp.477-483
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• 2005

This article describes a robust state estimation method which enables to produce reliable estimates in spite of heavy perturbation including plant uncertainty and external disturbances. The main idea is to combine the standard state estimator with the perturbation observer in the estimator frame. The perturbation observer reflects equivalent quantity of plant uncertainty and external disturbances during the estimation process so that the state estimator dynamics gets as close as possible to the real plant dynamics. The robust state estimator proposed in this paper is given in a recursive discrete-time form which is very useful fur implementation purpose. In terms of the error dynamics derived for the robust state estimator, we discuss the stability issue and noise sensitivity. The effectiveness and practicality of the robust state estimator are verified through numerical examples and experimental results.

• 자동차안전학회지
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• 제14권4호
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• pp.16-20
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• 2022

Accurate state estimation is important for autonomous driving. However, the estimation error increases in situations that a lot of longitudinal slip occurs. Therefore, this paper presents a vehicle state estimation method using an Extended Kalman Filter. The filter estimates the states of the host vehicle robust to wheel slip. It utilizes the measurements of the four-wheel rotational speeds, longitudinal acceleration, yaw-rate, and steering wheel angle. Nonlinear measurement model is represented by Ackermann Model. The main advantage of this approach is the accurate estimation of yaw rate due to the measurement of the steering wheel angle. The proposed algorithm is verified in scenarios of autonomous emergency braking (AEB), lane change (LC), lane keeping (LK) using an automated vehicle. The results show that the proposed algorithm guarantees accurate estimation in such scenarios.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1994년도 하계학술대회 논문집 A
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• pp.468-471
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• 1994

This paper presents the digital implementation of an optimal and robust VSS controller with sliding observer. Firstly, a discrete-time VSS control law which enables the system state to move into a sliding sector where the closed-loop system is stable is designed. Then optimal control theory is used to design an optimal sliding sector. Secondly, a sliding observer which provide robust state estimation against model-plant mismatches due to parameter uncertainties is designed for the sampled-data multivariable systems. Finally, modified sliding observer which effectively reduce chattering of state variables in state estimation was proposed. The proposed scheme was applied 10 a two degree of freedom inverted pendulum with passive joint to verify robust motion control. Computer simulation results confirm the viability of the proposed observer-based controller.

• 제어로봇시스템학회논문지
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• 제14권8호
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• pp.837-841
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• 2008

This paper presents a robust reduced order state observer for a class of Lipschitz nonlinear systems with external disturbance. Sufficient conditions on the existence of the proposed observer are characterized by linear matrix inequalities. It is also shown that the proposed observer design can reduce the effect on the estimation error of external disturbance up to the prescribed level. Finally, a numerical example is provided to verify the proposed design method.

• 제어로봇시스템학회논문지
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• 제12권3호
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• pp.201-207
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• 2006

A robust Kalman filtering method for uncertain stochastic systems is suggested by adopting a perturbation estimation process which is to reconstruct total uncertainty with respect to the nominal state transition equation. The predictor and corrector of discrete Kalman filter are reformulated with the perturbation estimator. Successively, the state and perturbation estimation error dynamics and the corresponding error covariance propagation equations are derived as well. Finally we have the recursive algorithm of Combined Kalman Filter-Perturbation Estimator (CKF). The proposed combined Kalman filter-perturbation estimator has the property of integrating innovations and the adaptation capability to system uncertainties. A numerical example is shown to demonstrate the effectiveness of the proposed scheme.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1477-1480
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• 1997

This paper is concerned with the design of a suboptimal robust Kalman filter using LMI approach for system models in the state space, which are subjected to parameter uncertainties in both the state and measurement atrices. Under the assumption that augmented system composed of the uncertain system and the state estimation error dynamics should be stable, a Lyapunov inequality is obtained. And from this inequaltiy, the filter design problem can be transformed to the gneric LMI problems i.e., linear objective minimization problem and generalized eigenvalue minimization problem. When applied to uncertain linear system modles, the proposed filter can provide the minimum upper bound of the estimation error variance for all admissible parameter uncertainties.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회A
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• pp.1103-1108
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• 2007

This paper describes a robust model-based roll state estimator for application to the detection of impending vehicle rollover. The roll state estimator is based on a 2-D bicycle model and a roll model to estimate the maneuver-induced vehicle roll motion. The measurement signals are lateral acceleration, yaw rate, steering angle, and vehicle speed. Vehicle mass is adapted to obtain robust performance of the estimator. Computer simulation is conducted to evaluate the proposed roll state estimator by using a validated vehicle simulator. It is shown that the roll state estimator shows robust performance without exact vehicle mass information.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.130-130
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• 2000

This paper presents a robust fault diagnosis and fault tolerant control lot the actuator and sensor faults in the closed-loop systems affected by unknown inputs or disturbances. The fault diagnostic scheme is based on the residual set generation by using robust Parity space approach. Residual set is evaluated through the threshold test and then fault is isolated according to the decision logic table. Once the fault diagnosis module indicates which actuator or sensor is faulty, the fault magnitude is estimated by using the disturbance-decoupled optimal state estimation and a new additive control law is added to the nominal one to override the fault effect on the system. Simulation results show that the method has definite fault diagnosis and fault tolerant control ability against actuator and sensor faults.

• 대한전기학회논문지:전력기술부문A
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• 제48권5호
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• pp.560-570
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• 1999

This paper formulates the suboptimal robust Kalman filtering problem into two coupled Linear Matrix Inequality (LMI) problems by applying Lyapunov theory to the augmented system which is composed of the state equation in the uncertain linear system and the estimation error dynamics. This formulations not only provide the sufficient conditions for the existence of the desired filter, but also construct the suboptimal robust Kalman filter. The proposed filter can guarantee the optimized upper bound of the estimation error variance for uncertain systems with parametric uncertainties in both the state and measurement matrices. In addition, this paper shows how the problem of finding the minimizing solution subject to Quadratic Matrix Inequality (QMI), which cannot be easily transformed into LMI using the usual Schur complement formula, can be successfully modified into a generic LMI problem.

• 전자공학회논문지
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• 제52권5호
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• pp.214-221
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• 2015

본 논문에서는 측정 잡음에 의해서 출력 신호가 왜곡된 이산 시간 선형 스위치드 시스템의 스위칭 신호와 상태 변수를 관측하기 위한 강인한 추정 및 검출 기법을 제시한다. 첫째, 최소 거리 기준에 기초한 작동 모드 추정 알고리즘이 제안된다. 이 과정에서 모드의 추정값을 얻게 되고, 이를 이용하여 상태 변수 또한 관측할 수 있게 된다. 둘째, 주어진 스위치드 시스템의 작동 모드 변화 시점, 즉, 스위칭이 언제 발생하는지 검출하는 알고리즘이 고안된다. 시스템에 인가되는 측정 잡음이 유계라는 가정 하에, 제안된 추정 알고리즘은 주어진 스위치드 시스템의 작동 모드를 정확하게, 그리고 상태 변수를 근사적으로 추정하며, 검출 알고리즘은 실제 스위칭이 발생한 후, 미리 정해진 지연 시간이 지나가기 전에 스위칭 시간을 검출할 수 있다.