• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.471-478
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• 2007

In this paper, an optimal Wiener-Hopf decoupling controller formula is obtained which is expressed in terms of rational matrices, thereby readily allowing the use of state-space algorithms. To this end, the characterization formula for the class of all realizable decoupling controller is formulated in terms of rational functions. The class of all stabilizing and decoupling controllers is parametrized via the free diagonal matrices and the optimal decoupling controller is determined from these free matrices.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.690-693
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• 2005

In this paper, a hybrid PD-servo state feedback control algorithm for swing up inverted pendulum system is proposed. It consists of two parts. The first part is the PD position control for swinging up the pendulum from the natural pendent position to around the upright position and the second part is the servo state feedback control for stabilizing the inverted pendulum in upright position. The first controller is PD controller and it is tuned to control the position of the pendulum by moving the cart back and forth until the pendulum swings up around the upright position. Then the second controller will be switched to stabilize the inverted pendulum in its upright position. The controller in this stage is the servo state feedback controller designed by pole placement. Experimental results of PD type swinging up control system, of stabilizing servo state feedback control system and of the proposed hybrid PD-servo state feedback control system to swing up and stabilize inverted pendulum show that the proposed method is effective and reliable for actual implementation while it is simple.

• Journal of Advanced Marine Engineering and Technology
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• v.34 no.2
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• pp.310-317
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• 2010

In this paper, we propose a method for swing-up and stabilization of a SESIP(Self-Erecting Single Inverted Pendulum) system which is one of the typical nonlinear systems. We use PV(Proportional velocity) controller for swinging up the pendulum and employ a PI-type state-feedback controller for stabilizing the pendulum. Control is switched to a stabilizing controller, which is designed to balance the inverted position of pendulum and the cart position to the near vertical position. Computer simulations are performed to illustrate the control performance of the proposed scheme.

• Proceedings of the KWS Conference
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• 2001.10a
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• pp.11-14
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• 2001

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1688-1691
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• 1997

In the time-invarient system, the adaptive controller was designed for the non-tracking error in the 1980's. In this study, the Model Reference Adaptive Control using on-line processing method is used to identify the coefficients of the model, and the Robust Controller (H.inf.) is designed to stabilize the rigid body and the flexible body of satellite, which can be perturbed due to disturbance, etc. The result obtained by H.inf. controller is compared with that of the PI(Proportional and Intergation) controller which is commonly used for stabilizing satellite.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.1-9
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• 1996

In this paper, a parameter identification method for a discrete-time linear system using multi-layer neural network is proposed. The parameters are identified with the combination of weights and the output of neuraons of a neural network, which can be used for a linear and a nonlinear controller. An adaptive output tracking architecture is designed for the linear controller. And, the nonlinear controller. A sliding mode control law is applied to the stabilizing the nonlinear controller such that output errors can be reduced. The effectiveness of the proposed control scheme is illustrated through simulations.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.112-115
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• 1989

This paper proposes a learning control algorithm for trajectory tracking of large scale system. The controller using only localized informations is composed of stabilizing controller and iterative learning controller. Stabilization and convergence of each subsystem is assured under some conditions which are inequalities of inter-connection terms and learning controller gain.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.535-535
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• 2000

This paper presents an adaptive receding horizon H$_{\infty}$ controller for the linear parameter varying systems in the deterministic environment, which combines a parameter range estimator and a robust receding horizon H$_{\infty}$ controller using the parameter bounds. Using parameter set inclusion and terminal inequality condition, the closed-loop system stability is guaranteed. It is shown that the stabilizing adaptive receding horizon H$_{\infty}$ controller guarantees the H$_{\infty}$ norm bound.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.24-34
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• 2008

A novel neural network model, termed the standard neural network model (SNNM), similar to the nominal model in linear robust control theory, is suggested to facilitate the synthesis of controllers for delayed (or non-delayed) nonlinear systems composed of neural networks. The model is composed of a linear dynamic system and a bounded static delayed (or non-delayed) nonlinear operator. Based on the global asymptotic stability analysis of SNNMs, Static state-feedback controller and dynamic output feedback controller are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based nonlinear systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Two application examples are given where the SNNMs are employed to synthesize the feedback stabilizing controllers for an SISO nonlinear system modeled by the neural network, and for a chaotic neural network, respectively. Through these examples, it is demonstrated that the SNNM not only makes controller synthesis of neural-network-based systems much easier, but also provides a new approach to the synthesis of the controllers for the other type of nonlinear systems.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1375-1381
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• 2007

In this paper, we propose a linear modeling and identical PD controller design scheme for the three-pole hybrid-type AMB recently developed in the laboratory, which consists of three permanent magnets, providing bias flux, three Hall diodes, measuring rotor displacements, and ring type permanent magnet bearing, stabilizing in axial and tilting directions. Along the three physical coordinates formed by three poles, we introduce the redundant coordinate system and three identical decoupled controllers to construct linear model. The experiments are also carried out in order to verify the effectiveness of proposed controller in stabilizing the transient and steady state response of rotor.