• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.4
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• pp.17-26
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• 1996

A submerged vehicle which is a nonlinear multivariable system must be designed to be roubst against inner-outer perturbations and hydrodynamic disturbances induces maneuvering operation. But a practical design of motion controller is limited by both mathematical modeling error and linearization errors. Performance of a motion controller based on traditional design method is very poor when the vehicle motion is under wave force distrubacnes near sea surface. Therefore, this ppaer proposes a design method of $^{\infty}$ controller under model uncertainty and sea wave disturbances. performance of the controllers by both computer simulation and HILS (hardwave in the loop simulation) shows that $H^{\infty}$ controller is more robust than PID controller under model uncertainty and high sea state...

• Journal of computational fluids engineering
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• v.9 no.2
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• pp.52-61
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• 2004

In the present study, a solution algorithm for the computation of unsteady flows on unstructured meshes is presented. Dual time stepping is incorporated to achieve the second-order temporal accuracy while reducing errors associated with linearization and factorization. This allows any time step size, which is suitable for considering physical phenomena of interest. The Gauss-Seidel scheme is used to solve the linear system of equations. A special treatment based on spring analogy is made to handle meshes with high aspect-ratio cells. The present method was validated by comparing the results with experimental data and those obtained from rigid motion.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.7
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• pp.950-955
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• 2014

In this paper, we propose a drug treatment protocol for the three state HIV infection model that explicitly includes the concentration of healthy T cells, infected T cells, and HIV. While most of the previous methods are not able to achieve the treatment goal in the presence of modelling errors, the proposed method is designed so as to compensate for the model uncertainties. Based on the Jacobain linearization of nonlinear HIV infection model, disturbance observer(DOB) based control is employed to design the drug treatment for the HIV patients. Computer simulation is carried out for nonlinear model in order to compare the performance of the proposed method with that of the conventional technique. The simulation results show that, in the presence of parameter uncertainties, the substantial improvement in the performance can be achieved by the proposed DOB controller.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.514-518
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• 2005

In this paper an adaptive fuzzy PI controller with feedback linearizing meth od is implemented to controlling flux and torque separately in induction motor. In this paper first decoupling of torque and flux which are outputs to be controlled, is achieved by using feedback linearization methodology. Then for reducing the effect of noise and rejection of disturbance, main part of controller which is adaptive PI fuzzy controller, is designed. Coefficients of PI controller are determined by defined fuzzy rules due to error dynamic. Inputs of fuzzy system are defined sliding surfaces which consist of torque and flux errors. The main contribution of this paper is effect reduction of noise and disturbance on torque and flux which is based on fuzzy logic and nonlinear control. At last the effectiveness of the proposed control scheme in presence of noise and load disturbance is simulated and comprised to applying sliding method. The results verify better effectiveness of the proposed method for effect reduction of noise and disturbance.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1955-1959
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• 2005

This paper presents a systematic analysis and a simple design of a robust adaptive control law for a class of non linear systems with modeling errors and a time-delay input. The theory for designing a robust adaptive control law based on input- output feedback linearization of non linear systems with uncertainties and a time-delay in the manipulated input by the approach of parameterized state feedback control is presented. The main advantage of this method is that the parameterized state feedback control law can effectively suppress the effect of the most parts of nonlinearities, including system uncertainties and time-delay input in the pp-coupling perturbation form and the relative order of non linear systems is not limited.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.501-506
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• 2001

In the procedure of the hydraulic control system analysis, a linearized approximate equation described by the first order term of Taylor's series has been widely used. Such a linearized equation is effective just near the operating point. In this study, the authors estimate computational errors in the process of applying the existing linearized equation stated above. For evaluating the computational accuracy in practical applications of the linearized equations, dynamic behaviors of hydraulic control systems are investigated through simulations with several kinds of representative hydraulic systems and the linearized equations suggested in this study.

• Proceedings of the KSME Conference
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• 2002.08a
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• pp.59-62
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• 2002

In the present study, solution algorithms for the computation of unsteady flows on an unstructured mesh are presented. Dual time stepping is incorporated to achieve the 2-nd order temporal accuracy while reducing the linearization and the factorization errors associated with a linear solver. Hence, any time step can be used by only considering physical phenomena. Gauss-Seidel scheme is used to solve linear system of equations. Rigid motion and spring analogy method fur moving mesh are all considered and compared. Special treatments of spring analogy for high aspect ratio cells are presented. Finally, numerical results for oscillating wing are compared with experimental data.

• International Journal of Control, Automation, and Systems
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• v.1 no.4
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• pp.520-526
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• 2003

In the case of a strapdown inertial navigation system (SDINS) with sizeable attitude errors, the uncertainty caused by linearization of the system degrades the performance of the filter. In this paper, a robust filter and various error models for the uncertainty are presented. The analytical characteristics of the proposed filter are also investigated. The results show that the filter does not require the statistical property of the system disturbance and that the region of the estimation error depends on a freedom parameter in the worst case. Then, the uncertainty of the SDINS is derived. Depending on the choice of the reference frame and the attitude error state, several error models are presented. Finally, various in-flight alignment methods are proposed by combining the robust filter with the error models. Simulation results demonstrate that the proposed filter effectively improves the performance.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1210-1215
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• 1990

This paper deals with the fault diagnosis problem in uncertain linear systems having undermodelling, linearization errors and noise inputs. The new approach proposed in this paper uses an appropriate test variable and the difference between system parameters which are estimated by the least squares method to locate the fault. The singular value decomposion is used to decouple the correlation between the estimated system parameters and to observe the trend of parameter changes. Some simulations applied to aircraft ergines show good allocation of the fault even though the system model has significant uncertainties. The feature of the approach is to diagnose the uncertain system through simple parameter operations and not to need complex calculations in the diagnosis procedure as compared with other methods.

• Journal of Mechanical Science and Technology
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• v.20 no.5
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• pp.591-601
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• 2006

In this paper, we present a new control methodology for perturbed crane systems. Nonlinear crane systems are transformed to linear models by feedback linearization. An inverse dynamic equation is applied to compute the system PD control force. The PD control parameters are selected based on a nominal model and are therefore suboptimal for a perturbed system. To achieve the desired performance despite model perturbations, we construct a neural network auxiliary controller to compensate for modeling errors and disturbances. The overall control input is the sum of the nominal PD control and the neural auxiliary control. The neural network is iteratively trained with a perturbed system until acceptable performance is attained. We apply the proposed control scheme to 2- and 3-degree-of-freedom (D.O.F.) crane systems, with known bounds on the payload mass. The effectiveness of the control approach is numerically demonstrated through computer simulation experiments.