• Journal of Institute of Control, Robotics and Systems
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• v.8 no.5
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• pp.351-356
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• 2002

This papaer deals with a model reduction problem for linear systems with time varying parameters. For this problem, a controllability Grammian and an observability Grammian are introduced and computed by solving linear matrix inequalities. Using the controllability/observability Grammian, a balanced state space realization for linear parameter varying systems is obtained. From the balanced state space realization, a reduced model can be obtained by truncating not only states but also time varying parameters and an upper bound of the model reduction error is derived as well.

• Journal of Mechanical Science and Technology
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• v.17 no.12
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• pp.1886-1893
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• 2003

This paper examines the parameter observability of a calibration system that consrains a mobile platform to a planar table to take the calibration data. To improve the parameter observability, we find the optimal configurations providing the calibration with maximum contribution. The QR-decomposition is used to compute the optimal configurations that maximize the linear independence of rows of an observation matrix. The calibration system is applied to the parallel type manipulator constructed for a machining center. The calibration results show that all the necessary kinematic parameters assigned in a Stewart-Gough platform are identifiable and convergent to desirable accuracy.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1512-1517
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• 2003

This paper presents the calibration for the parallel typed tilting table. The calibration system needs only simple sensing device which is a digital indicator to measure the orientation of a table. The calibration algorithm is developed by a measurement operator. It eliminates the concern about the poor parameter observability due to a large number of parameters of parallel-mechanism. This paper uses the QR-decomposition to find the optimal calibration configurations maximizing the linear independence of rows of a observation matrix. The number of identifiable parameters is examined by the rank of the observation matrix, which represents the parameter observability. The method is applied to a Parallel-typed Tilting Table and all the necessary kinematic parameters are identifiable.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.5
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• pp.326-332
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• 2003

This paper deals with a model reduction for linear systems with time varying delayed states. A generalized controllability and observability gramians are defined and obtained by solving linear matrix inequalities. Using the generalized controllability and observability gramians, the balanced state space equation is realized. The reduced model can be obtained by truncating states in the balanced realization and the upper bound of model approximation error is also presented. In order to demonstrate efficacy of the suggested method, a numerical example is performed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.337-340
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• 2001

In this paper, we consider the observability conditions for the following nonlinear fuzzy neutral functional differential equations : (equation omitted), where x(t) is state function on E$\_$N/$\^$2/, u(t) is control function on E$\_$N/$\^$2/ and nonlinear continuous functions f:J C$\_$0/ E$\_$N/$\^$2/, k:J C$\_$0/ E$\_$N/$\^$2/ are satisfies global Lipschitz conditions.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.2
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• pp.344-351
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• 2017

This paper presents a method to estimate steady wind and airspeed bias error using an aircraft with GPS and airspeed sensor. The estimation uses the vector relation between the inertial, air, and wind velocities through a novel design of an extended Kalman filter. The observability analysis is also presented to show that the aircraft is required to keep changing its flight direction for the desired observability. The feasibility and performance of the proposed algorithm is demonstrated through simulations and flight experiments.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.351-354
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• 1996

A new target adaptive guidance (TAG) algorithm is proposed to engage the aim point formed by adding a bias to the information from an infrared (IR) seeker for improving passive homing guidance effectiveness. The TAG algorithm utilizes an observability enhancing mid-course guidance algorithm to obtain convergent estimates of state variables involved particularly in range channel otherwise unavailable from passive sensors. Simulation results indicate that the TAG algorithm provides improved terminal effectiveness without computational complexities.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.10a
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• pp.353-354
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• 2006

• Korean Journal of Chemical Engineering
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• v.35 no.12
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• pp.2327-2335
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• 2018

Fluid catalytic cracking (FCC) is an important chemical process that is widely used to produce valuable petrochemical products by cracking heavier components. However, many difficulties exist in modeling the FCC process due to its complexity. In this study, a dynamic process model of a FCC process is suggested and its structural observability is analyzed. In the process modeling, yield function for the kinetic model of the riser reactor was applied to explain the product distribution. Hydrodynamics, mass balance and energy balance equations of the riser reactor and the regenerator were used to complete the modeling. The process model was tested in steady-state simulation and dynamic simulation, which gives dynamic responses to the change of process variables. The result was compared with the measured data from operating plaint. In the structural analysis, the system was analyzed using the process model and the process design to identify the structural observability of the system. The reactor and regenerator unit in the system were divided into six nodes based on their functions and modeling relationship equations were built based on nodes and edges of the directed graph of the system. Output-set assignment algorithm was demonstrated on the occurrence matrix to find observable nodes and variables. Optimal locations for minimal addition of measurements could be found by completing the whole output-set assignment algorithm of the system. The result of this study can help predict the state more accurately and improve observability of a complex chemical process with minimal cost.

• Smart Structures and Systems
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• v.30 no.4
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• pp.339-351
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• 2022

Evaluating the current condition of existing structures is of primary importance for economic and safety reasons. This can be addressed by Structural System Identification (SSI). A reliable static SSI depends on well-designed sensor configuration and loading cases, as well as efficient parameter estimation algorithms. Static SSI by the Measurement Error-Minimizing Observability Method (MEMOM) is a model-based deterministic static SSI method that could estimate structural parameters from static responses. In the current state of the art, this method is only applicable when structures are subjected to one loading case. This might lead to lack of information in some local regions of the structure (such as the null curvatures zones). To address this issue, the SSI by MEMOM using multiple loading cases is proposed in this work. Observability equations obtained from different loading cases are concatenated simultaneously and an optimization procedure is introduced to obtain the estimations by minimizing the discrepancy between the predicted response and the measured one. In addition, a Genetic-Algorithm (GA)-based Optimal Sensor Placement (OSP) method is proposed to tackle the OSP problem under multiple static loading cases for the very first time. In this approach, the Fisher Information Matrix (FIM)'s determinant is used as the metric of the goodness of sensor configurations. The numerical examples of a 3-span continuous bridge and a 13-story frame, are analyzed to validate the applicability of the extended SSI by MEMOM and the GA-based OSP method.