• Journal of Institute of Control, Robotics and Systems
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• v.5 no.7
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• pp.777-786
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• 1999

This work is concerned with the assignment of the desired eigenstructure for linear time-varying systems such as missiles, rockets, fighters, etc. Despite its well-known limitations, gain scheduling control appeared to be the focus of the research efforts. Scheduling of frozen-time, frozen-state controller for fast time-varying dynamics is known to be mathematically fallacious, and practically hazardous. Therefore, recent research efforts are being directed towards applying time-varying controllers. In this paper, ⅰ) we introduce a differential algebraic eigenvalue theory for linear time-varying systems, and ⅱ) we also propose an eigenstructure assignment scheme for linear time-varying systems via the differential Sylvester equation based upon the newly developed notions. The whole design procedure of the proposed eigenstructure assignment scheme is very systematic, and the scheme could be used to determine the stability of linear time-varying systems easily as well as provides a new horizon of designing controllers for the linear time-varying systems. The presented method is illustrated by a numerical example.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.2
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• pp.88-95
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• 1996

It is inevitable for local systems to have deviations which represent interactions and modeling errors originated from the decomposition process of a large scale system. This paper presents a decentralized control scheme for interconnected systems using local linear models and a neuro-coordinator. In the proposed method, the local system is composed of a linear model and unknown deviations caused by linearizing the subsystems around operating points or by estimating parameters of the subsystems. Because the local system has unmeasurable deviations we define a local reference model which consists of a local linear model and a neural network to estimate the deviations indirectly. The reference model is reformed into a linear model which has no deviations through a transformation of input variables and we obtain an optimum feedback control law which minimizes a local performance index. Finally, we derive a decentralized feedback control law which consists of local linear states and neural network outputs. In the decentralized control, the neuro-coordinator generates a corrective control signal to cancel the effect of deviations through backpropagation learning with the errors obtained from the differences of the local system outputs and reference model outputs. Also, the stability of local system is proved by the degree of learning of the neural network under an assumption on a neural network learning index. It is shown by computer simulations that the proposed control scheme can be applied successfully to the control of a biased two-link planar robot manipulator.

• Journal of the Korean Mathematical Society
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• v.52 no.3
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• pp.469-487
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• 2015

We consider a model of HIV infection with various compartments, including target cells, infected cells, viral loads and immune effector cells, to describe HIV type 1 infection. We show that the proposed model has one uninfected steady state and several infected steady states and investigate their local stability by using a Jacobian matrix method. We obtain equations for adjoint variables and characterize an optimal control by applying Pontryagin's Maximum Principle in a linear control problem. In addition, we apply techniques and ideas from linear optimal control theory in conjunction with a direct search approach to derive on-off HIV therapy strategies. The results of numerical simulations indicate that hybrid on-off therapy protocols can move the model system to a "healthy" steady state in which the immune response is dominant in controlling HIV after the discontinuation of the therapy.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.699-703
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• 2004

The autopilot system targets decreasing labor, working environment, service safety security and elevation of service efficiency. Ultimate purpose is minimizing number of crew for guarantee economical efficiency of shipping service. Recently, being achieving research about Course Keeping Control, Track Keeping Control, Roll-Rudder Stabilization, Dynamic ship Positioning and Automatic Mooring Control etc. which compensate nonlinear characteristic using optimizing control technique. And application research is progressing using real ship on actual field. Relation of Rudder angle which adjusted by Steering Machine and ship-heading angle are non-linear. And, Load Condition of ship acts as non-linear element that influence to Parameter of ship. Also, because the speed of a current and direction of waves, velocity and quantity of wind etc. that id disturbance act in non-linear form, become factor who make service of shipping painfully. Therefore, service system of shipping requires robust control algorithm that can overcome nonlinearity. In this paper, Using GA algorithm,design autopilot system of ship that could overcome the non-linear factor of ship and disturbance and examined result through simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.11
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• pp.1048-1052
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• 2007

This paper presents a rank-constrained linear matrix inequality(LMI) approach to simultaneous linear-quadratic(LQ) optimal control by static output feedback. Simultaneous LQ optimal control is formulated as an LMI optimization problem with a nonconvex rank condition. An iterative penalty method recently developed is applied to solve this rank-constrained LMI optimization problem. Numerical experiments are performed to illustrate the proposed method, and the results are compared with those of previous work.

• Journal of Mechanical Science and Technology
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• v.16 no.12
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• pp.1561-1575
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• 2002

A reduced-order linear feedback controller, which is used to control the linear disturbance in two-dimensional plane Poiseuille flow, is applied to a boundary layer flow for stability control. Using model reduction and linear-quadratic-Gaussian/loop-transfer-recovery control synthesis, a distributed controller is designed from the linearized two-dimensional Navier-Stokes equations. This reduced-order controller, requiring only the wall-shear information, is shown to effectively suppress the linear disturbance in boundary layer flow under the uncertainty of Reynolds number. The controller also suppresses the nonlinear disturbance in the boundary layer flow, which would lead to unstable flow regime without control. The flow is relaminarized in the long run. Other effects of the controller on the flow are also discussed.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.1
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• pp.13-18
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• 2005

This paper considers a design problem of the repetitive control system for linear systems with time-varying norm bounded uncertainties. Using the Lyapunov functional for time-delay systems, a sufficient condition ensuring robust stability of the repetitive control system is derived in terms of an algebraic Riccati inequality (ARI) or a linear matrix inequality (LMI). Based on the derived condition, we show that the repetitive controller design problem can be reformulated as an optimization problem with an LMI constraint on the free parameter.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.377-384
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• 2007

This paper studies on the relation between linearization methods and controller gains in the looper ILQ(lnverse Linear Quadratic optimal control) system for hot strip finishing mills. Firstly, two linear models arc respectively derived by a linearization method using Taylor's series expansion and a static state feedback linearization method, respectively, and the linear models are compared with the nonlinear model. Secondly, the looper servo controllers are respectively designed on the basis of two linearization models. Finally, the relation between the performances of two ILQ servo controllers and the linearization methods, and the structures and control gains of two controllers are evaluated by a computer simulation.

• Nuclear Engineering and Technology
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• v.52 no.3
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• pp.552-559
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• 2020

This paper presents an observer-based chattering free robust optimal control scheme to regulate the total power of a nuclear reactor. The non-linear model of nuclear reactor is linearized around a steady state operating point to obtain a linear model for which an optimal second order integral sliding mode controller is designed. A second order integral sliding mode observer is also designed to estimate the unmeasurable states. In order to avoid the chattering effect, the discontinuous input of both observer and controller are designed using the super-twisting algorithm. The proposed controller is realized by combining an optimal linear tracking controller with a second order integral sliding mode controller to ensure minimum control effort and robustness of the closed-loop system in the presence of uncertainties. The condition for the selection of gains of discontinuous control based on the super-twisting algorithm is derived using a strict Lyapunov function. Performance of the proposed observer based control scheme is demonstrated through non-linear simulation studies.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.997_998
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• 2009

Nowadays DC-DC converter has been used widely in electronic production. It has a high requirement in wide input voltage, load variations, stability, providing a fast transient response and the most important thing is that it can be applied easily and efficiently. However, it is not easy to be controlled because of nonlinear system. This study introduces a fuzzy linear control design method for nonlinear systems with optimal $H^{\infty}$ robustness performance. First, the Takagi and Sugeno fuzzy linear model is employed to approximate a nonlinear system. Next, based on the fuzzy linear model, a fuzzy controller is developed to stabilize the nonlinear system, and at the same time the effect of external disturbance on control performance is attenuated to a minimum level. Thus based on the fuzzy linear model, ��$H^{\infty}$ performance design can be achieved in nonlinear control systems. Linear matrix inequality (LMI) techniques are employed to solve this robust fuzzy control problem. PI control structure is used and the control gains are determined based on $H^{\infty}$ control.