• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.26.4-26
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• 2001

This paper proposes a dynamic observer that is applicable to linear time-invariant systems subject to unknown inputs. The proposed method utilizes the output feedback control structure to design an unknown input observer. We name it as the dynamic unknown input observer(UIO). The dynamic UIO can be designed easily compared to the usual static UIO, and the system response could be improved.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1149-1151
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• 1996

In the last several years, considerable attention has been focused on the problem of designing observers for linear systems with unknown inputs. Since UIO(unknown inputs observer) has the derivative of the outputs, it is very sensitive to measurement noises. Therefore this note propose an algebraic approach to UIO design to alleviate the prescribed problems. Since the proposed method has simple form to estimate state and unknown input and robustness to sensor noise, we believe that it is very attractive in practice.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.296-298
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• 1993

In this paper, a new design procedure is proposed for constructing the observer for linear discrete-time systems with unknown inputs. An existence condition for the suggested observer is also presented. The observer design procedure does not use any transformation technique, augmentation technique and has a clear physical meaning, so it is easy to understand and to design the observer. It directly estimates the unknown inputs from that outputs only, so it is different from other pre-works in which the reconstruction of the unknown inputs arc performed with the additional output derivatives.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.6
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• pp.411-416
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• 2016

This paper presents the design methodology of an unknown input observer for Lipschitz nonlinear systems with unknown inputs in the framework of convex optimization. We use an unknown input observer (UIO) to consider both nonlinearity and disturbance. By deriving a sufficient condition for exponential stability in the linear matrix inequality (LMI) form, existence of a stabilizing observer gain matrix of UIO will be assured by checking whether the quadratic stability margin of the error dynamics is greater than the Lipschitz constant or not. If quadratic stability margin is less than a Lipschitz constant, the coordinate transformation may be used to reduce the Lipschitz constant in the new coordinates. Furthermore, to reduce the maximum singular value of the observer gain matrix elements, an object function to minimize it will be optimally designed by modifying its magnitude so that amplification of sensor measurement noise is minimized via multi-objective optimization algorithm. The performance of UIO is compared to a nonlinear observer (Luenberger-like) with an application to a flexible joint robot system considering a change of load and disturbance. Finally, it is validated via simulations that the estimated angular position and velocity provide true values even in the presence of unknown inputs.

• Smart Structures and Systems
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• v.30 no.3
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• pp.273-286
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• 2022

In recent years, vision-based monitoring has received great attention. However, structural identification using vision-based displacement measurements is far less established. Especially, simultaneous identification of structural systems and unknown excitation using vision-based displacement measurements is still a challenging task since the unknown excitations do not appear directly in the observation equations. Moreover, measurement accuracy deteriorates over a wider field of view by vision-based monitoring, so, only a portion of the structure is measured instead of targeting a whole structure when using monocular vision. In this paper, the identification of structural system and excitations using vision-based displacement measurements is investigated. It is based on substructure identification approach to treat of problem of limited field of view of vision-based monitoring. For the identification of a target substructure, substructure interaction forces are treated as unknown inputs. A smoothing extended Kalman filter with unknown inputs without direct feedthrough is proposed for the simultaneous identification of substructure and unknown inputs using vision-based displacement measurements. The smoothing makes the identification robust to measurement noises. The proposed algorithm is first validated by the identification of a three-span continuous beam bridge under an impact load. Then, it is investigated by the more difficult identification of a frame and unknown wind excitation. Both examples validate the good performances of the proposed method.

• Proceedings of the KAIS Fall Conference
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• 2006.05a
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• pp.307-310
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• 2006

We consider the problem of constructing observers for nonlinear systems with unknown inputs. It is shown that under some conditions, there exists an observer estimating the states of nonlinear systems with unknown inputs. Nonlinear observer design method using observer error linearization and the design technique of unknown input observer(UIO) for linear systems are used to derive conditions. Some illustrative examples are included. In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller.The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.646-647
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• 2005

A recently published paper demonstrates the utility of allowing delays in state estimators for linear systems with unknown inputs. In this note, we point out an error in the derivation of the estimator.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.2
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• pp.35-44
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• 1994

In this paper, an observer for linear discrete systems with unknown inputs is presented. The suggested observer can estimate the system state vector and the unknown inputs simultaneously. As an extension of the observer, a new fault diagnosis observer for linear discrete systems with structured uncertainty is presented. The fault diagnosis observer can detect and identify the actuator and the sensor faults as well. The stability conditionsand the design methods of the each observers are presented and the usability of the observers is shown via numerical examples.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1138-1144
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• 2011

This article deals with the problem of fault detection of a spacecraft's actuators. The authors introduce an extended unknown input observer for nonlinear systems. This is an extended form of unknown input observers which are used for linear systems. Since faults are not available, those are considered as unknown inputs. Unknown input observers can estimate states without full information of inputs if some conditions are satisfied. The authors suggest a continuous-time extended UIO (eUIO) and prove the convergence of state estimation errors. Since the dynamic equation of a spacecraft is nonlinear, an extended UIO can be applied. Three eUIOs are designed to monitor three reaction wheels. The moving averages of each eUIO's residuals are selected for decision logic. The proposed method is verified by numerical simulations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.7
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• pp.1247-1253
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• 2008

A design method of a T-S fuzzy model based reduced order nonlinear unknown input observer(NUIO) is presented. The fuzzy NUIO is designed based on the parallel distributed compensation(PDC) concept. It consists of a number of the linear UIOs, each of which is designed for each local linear model in the T-S fuzzy model of a class of nonlinear systems. The fuzzy NUIO provides not only the state estimates insensitive to the unknown inputs, for example, disturbances and faults etc., but also the estimates of the unknown inputs. Therefore, It can be employed in the state feedback control and disturbance rejection control of a class of nonlinear systems with unknown disturbances. It also applied to the robust residual generation for the fault detection and isolation systems and to the design of fault tolerant control systems. As an example, the NUIO is applied to an inverted pendulum system to show the state and disturbance estimation performance and to illustrate the fuzzy reduced order NUIO design method.