• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2086-2088
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• 2002

This paper deals with an algebraic approach for unknown inputs observer by using Haar functions. In the algebraic UIO(unknown input observer) design procedure, coordinate transformation method is adopted to derive the reduced order dynamic system which is decoupled unknown inputs and Haar function and its integral operational matrix is applied to avoid additional differentiation of system outputs.

• International Journal of Control, Automation, and Systems
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• v.4 no.2
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• pp.178-186
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• 2006

We propose and analyze a robust adaptive fuzzy controller for nonlinear systems without a priori knowledge of the sign of the input gain function. No assumptions are made about the type of nonlinearities of the system, except that such nonlinearities are smooth. The uncertain nonlinearities are captured by the fuzzy systems that have been proven to be universal approximators. The proposed control scheme completely overcomes the singularity problem that occurs in the indirect adaptive feedback linearizing control. Projection in the estimated parameters and switching in the control input are both not required. The stability of the closed-loop system is guaranteed in the Lyapunov viewpoint.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.108-114
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• 2002

This paper describes the design of a robust adaptive controller for a nonlinear dynamical system with unknown nonlinearities. These unknown nonlinearities are approximated by multilayered neural networks (MNNs) whose parameters are adjusted on-line, according to some adaptive laws far controlling the output of the nonlinear system, to track a given trajectory. The main contribution of this paper is a method for considering input constraint with a rigorous stability proof. The Lyapunov synthesis approach is used to develop a state-feedback adaptive control algorithm based on the adaptive MNN model. An overall control system guarantees that the tracking error converges at about zero and that all signals involved are uniformly bounded even in the presence of input saturation. Theoretical results are illustrated through a simulation example.

• Journal of Navigation and Port Research
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• v.32 no.2
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• pp.133-140
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• 2008

In this paper, a fault detection method for operator failures using the observation technique is proposed. The suggested algorithm is extended using the conventional sensor/actuator fault detection method. First, it is assumed that operator failure affects human work operations, as it is an external input signal. With this assumption, a human work model with operator failure is suggested. Second, an unknown input observer with proportional and integral gains is introduced. The characteristic of this observer of estimating an external signal without an exact input is shown, and the conditions for the detection of an operator failure are proposed. Finally, by simulating the container crane operations, it is verified that the observer can accurately detect an operator failure and estimate its magnitude from the given internal signal.

• Journal of the Earthquake Engineering Society of Korea
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• v.20 no.5
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• pp.341-349
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• 2016

Currently, researches are being actively conducted in assessing seismic performance of nuclear facilities in USA and Europe. In particular, applying this technique of assessing seismic performance to design of isolation systems in nuclear power plants is being performed and then ASCE 4 Draft (2013) is being revised accordingly in the United States. In order to satisfy the probabilistic performance objectives described by seismic responses with certain confidence levels (ASCE 43, 2005), the probability distributions of these responses have to be defined. What is the minimum number of input ground-motions to obtain the probability distribution precise enough to represent the unknown actual distribution? Theoretical basis, for how to determine the minimum number of input ground-motions for given a logarithmic standard deviation to approximate the unknown actual median of the log-normal distribution within a range of error at a certain level of confidence, is introduced by Huang et al. (2008). However, the relationship between the level of confidence and the range of error is not stated in the previous study. In this paper, based on careful reviews on the previous work, the relationship between the level of confidence and the range of error is logically and explicitly stated. Furthermore, this relationship is also applied to derive the minimum number of input ground-motions in order to approximate the unknown actual logarithmic standard deviation. Several recommendations are made for determining the minimum number of input ground-motions in probabilistic assessment on seismic performance of facilities in nuclear power plants.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.578-580
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• 1997

A new method of Fault Diagnosis in linear systems using unknown input observer design technique is presented. This method is based upon the fact that the structural uncertainties, actuator faults, and sensor faults of a linear system can be rewritten in unknown inputs. The proposed method can simultaneously estimate the state variables of an actual system, as well as the actuator and sensor faults.

• East Asian mathematical journal
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• v.16 no.2
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• pp.251-266
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• 2000

The problem of solution uniqueness to the task of determining unknown parameters of mathematical models from input-output observations is studied. This problem is known as structural identifiability problem. We offer a new approach for testing structural identifiability of linear state space models. The approach compares favorably with numerous methods proposed by other authors for two main reasons. First, it is formulated in obvious mathematical form. Secondly, the method does not involve unfeasible symbolic computations and thus allows to test identifiability of large-scale models. In case of non-identifiability, when there is a set of solutions to the task, we offer a method of computing functions of the unknown parameters which can be determined uniquely from input-output observations and later used as new parameters of the model. Such functions are called parametric functions capable of estimation. To develop the method of computation of these functions we use Lie group transformation theory. Illustrative example is given to demonstrate applicability of presented methods.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.48.3-48
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• 2002

1) Linear state feedback control design problem for plant with unknown deterministic disturbance is considered and a method to realize state feedback by using adaptive observer which estimates the unknown disturbance simultaneously is proposed. 2) From the viewpoint of practical application, we propose an extended adaptive observer with direct plant path from input to output, which is necessary to use the acceleration type sensors as plant output. 3) Theoretical result is confirmed by numerical simulation of 1-DOF vibration control system.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.177-182
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• 1989

In this paper a new adaptive control algorithm is derived, with the unknown manipulator and payload parameters being estimated online. In practice, we may simplify the algorithm by not explicity estimating all unknown parameters. Further, the controller must be robust to residual time-varying disturbance, such as striction or torque ripple. Also, the reference model is a simple douple integrator and the acceleration input for robot manipulator consists of a proportion and derivative controller for trajectory tracking purposes. The validity of this control is confirmed in simulation where two-link robot manipulator shows the robust performances in spite of the existing nonlinear interaction and unknown parametrictings