• Smart Structures and Systems
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• v.20 no.1
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• pp.1-10
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• 2017

Ionic Polymer Metal Composite (IPMC) is an electroactive polymer (EAP) and a promising candidate actuator for various potential applications mainly due to its flexible, low voltage/power requirements, small and compact design, and lack of moving parts. Although widely used in industry, this material requires accurate numerical models and knowledge of optimal control methods. This paper presents State-Dependent Riccati Equation (SDRE) approach as one of rapidly emerging methodologies for designing nonlinear controllers. Additionally, the present paper describes a novel method of Multi HGO Observer design. In the proposed design, the calculated position of the IPMC strip accurately tracks the target position, which is illustrated by the experiments. Numerical results and comparison with experimental data are presented and the effectiveness of the proposed control strategy is verified in experiments.

• Bulletin of the Korean Space Science Society
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• 2011.04a
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• pp.32.2-32.2
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• 2011

State dependent Riccati equation (SDRE) control technique has been widely used in the control society. Although it solves nonlinear optimal control problems, which minimizes state error and control efforts simultaneously, it has drawbacks when it is to be applied to the real time systems in that it requires much computational efforts. So the real time system whose computational ability is limited (for example, satellites) cannot afford to use SDRE controller. To solve this problem, a hybrid controller which is based on MSDRE (Modified SDRE) and ANFIS (Adaptive Neuro-Fuzzy Inference System) has been proposed by Abdelrahman et al. (2010). We propose a hybrid controller based on SDRE and ANFIS, and apply the hybrid controller to the hardware attitude simulator to perform a HIL (Hardware-In-the-Loop) simulation. Through HIL simulation, it is demonstrated that the hybrid controller satisfies the control requirement and the computation load is reduced significantly. In addition, the effects of statistical properties of the ANFIS training data to the performance of the ANFIS controller have been analyzed.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.295-298
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• 1996

In this paper, a self-tuning optimal control algorithm is proposed to retain the optimal performance of an active suspension system, when the vehicle has some time varying parameters and parameter uncertainties. We consider a 2 DOF time-varying quarter car model which has the parameter variation of sprung mass, suspension spring constant and suspension damping constant. Instead of solving algebraic riccati equation on line, we propose a neural network approach as an alternative. The optimal feedback gains obtained from the off line computation, according to parameter variations, are used as the neural network training data. When the active suspension system is on, the parameters are identified by the recursive least square method and the trained neural network controller designer finds the proper optimal feedback gains. The simulation results are represented and discussed.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.197-202
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• 1992

In this paper, we consider the synthesis of mixed H$_{2}$/H$_{\infty}$ controllers such that the closed-loop poles are located in a specified region in the complex plane. Using solution to a generalized Riccati equation and a change of variable technique, it is shown that this synthesis problem can be reduced to a convex optimization problem over a bounded subset of matrices. This convex programming can be further reduced to Generalized Eigenvalue Minimization Problem where Interior Point method has been recently developed to efficiently solve this problem..

• Journal of applied mathematics & informatics
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• v.18 no.1_2
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• pp.73-93
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• 2005

In this paper, we attempt to present a new numerical approach to solve non-linear backward stochastic differential equations. First, we present some definitions and theorems to obtain the conditions, from which we can approximate the non-linear term of the backward stochastic differential equation (BSDE) and we get a continuous piecewise linear BSDE correspond with the original BSDE. We use the relationship between backward stochastic differential equations and stochastic controls by interpreting BSDEs as some stochastic optimal control problems, to solve the approximated BSDE and we prove that the approximated solution converges to the exact solution of the original non-linear BSDE in two different cases.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.135-141
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• 2008

A new approach to $H_{\infty}$ multi-step prediction is developed by applying the innovation analysis theory. Although the predictor is derived by resorting to state augmentation, nevertheless, it is completely different from the previous works with state augmentation. The augmented state here is considered just as a theoretical mathematic tool for deriving the estimator. A distributed algorithm for the Riccati equation of the augmented system is presented. By using the reorganized innovation analysis, calculation of the estimator does not require any augmentation. A numerical example demonstrates the effect in reducing computing burden.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.622-628
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• 1999

This paper presented to design a robust turbo-generator control system using {{{{ { H}_{$\infty$ } }}}} control synthesis for improving small-signal stability. Application study of{{{{ { H}_{$\infty$ } }}}} control synthesis is more appropriate in this system since a turbo-generator system is usually operated under circumstance of unmeasurable modelling uncertainty and external disturbance. The{{{{ { H}_{$\infty$ } }}}} control theory was briefly reviewed for good understanding and the reasonable approach. The design results are simulated for a case study and to check the system performance in comparison with currently operating Lead/Lag filtered PSS performance.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.8
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• pp.965-971
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• 1999

This paper presented to design the power system stabilizer(PSS) for a turbo-generator system using LQG/LTR control synthesis for improving small-signal stability. Application study of LGG/LTR control synthesis is more appropriate in this system since a turbo-generator system is usually operated under circumstance of unmeasurable uncertainties and external disturbance. The LQG/LTR control theory was briefly reviewed for good understanding and the reasonable design approach. The design results are simulated for a case study and to check the system performance in comparison with currently operating lead-lag filtered PSS performance.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1089-1091
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• 1996

This paper presents a design method of Kalman Filter on continuous nonlinear stochastic system via BPF(Block Pulse Function). When we design Kalman Filter on nonlinear stochastic system, we must linearize this systems. In this paper, we uses the adaptive approach scheme and BPF for linearizing of nonlinear system and solving the Riccati differential equation which is usually guite difficult. This method proposed in this paper is simple and have computational advantages. Furthermore this method is very applicable to analysis and design of Kalman Filter on nonlinear stochastic systems.

• Structural Engineering and Mechanics
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• v.56 no.4
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• pp.625-648
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• 2015

Modern super-tall buildings are more sensitive to strong winds. The evaluation of wind loads for the design of these buildings is of primary importance. A direct monitoring of wind forces acting on super-tall structures is quite difficult to be realized. Indirect measurements interpreted by inverse techniques are therefore favourable since dynamic response measurements are easier to be carried out. To this end, a Kalman filtering based inverse approach is developed in this study so as to estimate the wind loads on super-tall buildings based on limited structural responses. The optimum solution of Kalman filter gain by solving the Riccati equation is used to update the identification accuracy of external loads. The feasibility of the developed estimation method is investigated through the wind tunnel test of a typical super-tall building by using a Synchronous Multi-Pressure Scanning System. The effects of crucial factors such as the type of wind-induced response, the covariance matrix of noise, errors of structural modal parameters and levels of noise involved in the measurements on the wind load estimations are examined through detailed parametric study. The effects of the number of vibration modes on the identification quality are studied and discussed in detail. The made observations indicate that the proposed inverse approach is an effective tool for predicting the wind loads on super-tall buildings.