• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.185-189
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• 2007

This paper deals with a fault detection system design for nonlinear systems with uncertain time varying parameters modelled as a T-S fuzzy system. A coprime factorization for T-S fuzzy systems is defined and a residual generator is designed using a left coprime factor. A fault detection criteria derived from the residual generator is also suggested. In order to demonstrate the efficacy of the suggested method, the fault defection method is applied to an inverted pendulum system and computer simulations are performed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.356-361
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• 2006

This paper deals with a fault detection system design for nonlinear systems with uncertain time varying parameters modelled as a T-S fuzzy system. A coprime factorization for T-S fuzzy systems is defined and a residual generator is designed using a left coprime factor. A fault detection criteria derived from the residual generator is also suggested. In order to demonstrate the efficacy of the suggested method, the fault detection method is applied to an inverted pendulum system and computer simulations are performed.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.340-340
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• 2000

This paper aims at deriving an equivalent finite dimensional discrete-time system for H$_{\infty}$ type problem for sampled-data control systems. A widely used ph is based on the lifting technique, but it needs somewhat complicate computation. Instead this paper derives an equivalent finite-dimensional discrete-time system directly from a description of the sampled-data system which is achieved via a closed-loop expression of the worst-case intersample disturbance.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.66.4-66
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• 2001

In this paper we have fabricated the two-dimensional Rod Balancing System which expands conventional one-dimensional inverted pendulum control system and designed its controller. The X-axis cart and Y-axis bar of the Rod Balancing System, which is composed of X-Y table, are actuated through timing belt by each of two geared DC motors, and the rod mounted on a X-axis cart can be brought to the desired position and maintained in a vertical position by motor-control. For the control of the Rod Balancing System, we used a fuzzy logic controller that is an approach to systems control when the exact mathematical model of the plant is unknown or the mathematical model is too complex to understand.

• International Journal of Control, Automation, and Systems
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• v.5 no.6
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• pp.621-629
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• 2007

In the adaptive fuzzy control field for affine nonlinear systems, there are two basic configurations: direct and indirect. It is well known that the direct configuration needs more restrictions on the control gain than the indirect configuration. In general, these restrictions are difficult to check in practice where mathematical models of plant are not available. In this paper, using a simple extension of the universal approximation theorem, we show that the only required constraint on the control gain is that its sign is known. The Lyapunov synthesis approach is used to guarantee the stability and convergence of the closed loop system. Finally, examples of an inverted pendulum and a magnet levitation system demonstrate the theoretical results.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.144-149
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• 1993

This paper discusses a design of a nonlinear control for a class of single-input double-output nonlinear mechanical systems. When conventional linearization methods are applied to the mechanical systems, some problems of oscillation and unstable phenomena arise. The proposed nonlinear control system resolves these problems. In this design the eigenvalues of the closed-loop nonlinear system are assigned to desired locations and local asymptotic stability of the closed-loop system. is guaranteed. The design method is applied to an inverted pendulum system with a moving weight mechanism. Experimental results show that the proposed nonlinear controller is more effective for stability than the usual linear controller.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.136-138
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• 2006

Recently, network systems are widely used in several areas, and some considerable attentions have been directed to the Networked Control System(NCS). In NCS, network-induced delays are inevitable, and they sometimes degrade the performance of networked control systems to be a source of potential instability. In this paper, We proposes a compensation method for networked control system subject to network-induced delays by using a simple method, which is based on a sort of predictive strategy. To evaluate its feasibility and effectiveness, a real-time NCS for a rotary inverted pendulum is implemented via an Ethernet. Based on the experimental results. we show that the proposed simple method can be a practical and feasible solution to NCS design.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1165-1168
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• 1996

The neural network approach has been shown to be a general scheme for nonlinear dynamical system identification. Unfortunately the error surface of a Multilayer Neural Network(MNN) that widely used is often highly complex. This is a disadvantage and potential traps may exist in the identification procedure. The objective of this paper is to identify a nonlinear dynamical systems based on Radial Basis Function Networks(RBFN). The learning with RBFN is fast and precise. This paper discusses RBFN as identification procedure is based on a nonlinear dynamical systems. and A design method of model follow control system based on RBFN controller is developed. As a result of applying this method to inverted pendulum, the simulation has shown that RBFN can be used as identification and control of nonlinear dynamical systems effectively.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.862-864
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• 1999

Adaptive Fuzzy control system is very powerful in nonlinear system, but That system require exactly membership function and parameter. If the membership function and parameter are not exact, the system will generate chattering. Using the Pole assignment compensator can remove the chattering and steepest descent method can reduce the convergence time. In this Paper, this algorithm applicate to the Inverted pendulum, so save proof of algorithm that is to be vigorous.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.3
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• pp.40-50
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• 1996

By using the neural networks(NN) as system identifier, the on-line self tuning method for fuzzy controller(FC) is proposed. In theis method, the learning of NN is carried out during control operation of FC and the cinsequent parameters of FC is tuned on-line automatically by means of system output errors backpropagated through NN. The Sugeno fuzzy model with constants as consequent parameters is selected for simplifying computation. In procedures of parameter tuning, the gradient descent method is used and the gradient vectors for adjusting the weight of NN are transferred as controller output errors. To evaluate the performance, the proposed method is applied to the inverted pendulum system.