• Journal of IKEEE
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• v.18 no.2
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• pp.206-213
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• 2014

In this note, a new robust nonlinear output feedback variable structure controller is first systematically and generally designed for the output control of more affine uncertain nonlinear systems with mismatched uncertainties and matched disturbance. A transformed integral output feedback sliding surface with a most simple form is applied in order to remove the reaching phase problems. The closed loop exponential stability and the existence condition of the sliding mode on the integral output feedback sliding surface is investigated with a corresponding output feedback control input in Theorem 1. For practical application the continuous implementation of the control input is made by the modified saturation function. The effectiveness of the proposed controller is verified through a design example and simulation study.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.108-114
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• 2002

This paper describes the design of a robust adaptive controller for a nonlinear dynamical system with unknown nonlinearities. These unknown nonlinearities are approximated by multilayered neural networks (MNNs) whose parameters are adjusted on-line, according to some adaptive laws far controlling the output of the nonlinear system, to track a given trajectory. The main contribution of this paper is a method for considering input constraint with a rigorous stability proof. The Lyapunov synthesis approach is used to develop a state-feedback adaptive control algorithm based on the adaptive MNN model. An overall control system guarantees that the tracking error converges at about zero and that all signals involved are uniformly bounded even in the presence of input saturation. Theoretical results are illustrated through a simulation example.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.448-453
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• 2004

In this paper, an adaptive robust decentralized excitation control scheme is proposed to enhance the transient stability of a multi-machine power system. We employ a state model where the terminal voltage of each generator is regarded as part of the state. Using this state model, the proposed controller is obtained in two steps: firstly, a robust controller is designed for the nominal system with no interconnection terms; then an adaptive compensator is proposed to deal with those interconnection terms, whose upper bounds are estimated. The resulting adaptive scheme guarantees the practical stability of the closed-loop, and also the uniform ultimate boundedness in the presence of disturbances.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.390-392
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• 2009

This paper is concerned with the stabilization problem of nonlinear networked control systems with time-delay via Takagi-Sugeno(T-S) fuzzy control approach. The T-S fuzzy models are employed to represent nonlinear systems with Markovian jump parameters and time-delay. The purpose of this paper is to design a fuzzy controller such that the closed-loop Markovian jump fuzzy system is stochastically stable. Based on a stocastic Lyapunov function, stabilization sufficient conditions using a mode-independent fuzzy controller are derived for the Markovian jump fuzzy system in terms of Linear Matrix Inequalities(LMIs). Finally, a simulation example is presented to illustrate the effectiveness of the proposed method.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.329-331
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• 2009

This paper is concerned with the stabilization problem of nonlinear networked control systems with time-delay via Takagi-Sugeno(T-S) fuzzy control approach. The T-S fuzzy models are employed to represent nonlinear systems with Markovian jump parameters and time-delay. The purpose of this paper is to design a fuzzy controller such that the closed-loop Markovian jump fuzzy system is stochastically stable. Based on a stocastic Lyapunov function, stabilization sufficient conditions using a mode-independent fuzzy controller are derived for the Markovian jump fuzzy system in terms of Linear Matrix Inequalities(LMIs). Finally, a simulation example is presented to illustrate the effectiveness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.172.4-172
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• 2001

In this paper, the Generalized Predictive Control(GPC) method based on Fuzzy Neural Networks(FNNs) is presented for the control of chaotic nonlinear systems without precise mathematical models. In our method, FNNs is used as the predictor whose parameters are tuned by the error between the actual output of nonlinear chaotic system and that of FNNs model. The parameters of GPC controller are adjusted via the gradient descent method where the difference between the actual output and the reference signal is used as a control error. Finally, computer simulation on the representative continuous-time chaotic system(Duffing system) is presented to demonstrate the effectiveness of our chaos control method.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1833-1838
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• 1991

This paper presents a stabilization technique for unstable systems. An inverted pendulum, which is a typical unstable mechanical system, is considered and stabilized by a nonlinear control. The stabilization problem in this system is related to that in postural control of human being. In this paper, the variable structure control (VSC) is applied to the stabilization problem. Robustness by the VSC and that by a conventional linear feedback controller are compared.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.76-78
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• 2009

This paper considers the problem of global adaptive regulation for a class of nonlinear systems which have multiple unknown virtual control coefficient. By using a new parameter estimator and backstepping technique, we design a smooth state feedback control law, parameter update laws that estimate the unknown virtual control coefficients, and a continuously differentiable Lyapunov function which is positive definite and proper.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.249-257
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• 1995

In this paper, we demeonstrate that neural networks can be used effectively for the control of nonlinear dynamical system. To adaptively control a plant, there are two distinct approach. these are direct control and indirect control. Both direct and Indirect adaptive control are trained using static back propagation. In indirect, using the resulting identification model, which contains neural networks and linear dynamical elements as subsystems, the parameters of the controller are adjusted.

• Proceedings of the KAIS Fall Conference
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• 2006.05a
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• pp.307-310
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• 2006

We consider the problem of constructing observers for nonlinear systems with unknown inputs. It is shown that under some conditions, there exists an observer estimating the states of nonlinear systems with unknown inputs. Nonlinear observer design method using observer error linearization and the design technique of unknown input observer(UIO) for linear systems are used to derive conditions. Some illustrative examples are included. In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller.The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system