• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.42-49
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• 2000

This paper presents the application of a FARMA controller to stabilization of nonlinear Two-Generator Five-Bus power Systems. The control rules and the membership functions of the FARMA controller are generated automatically without using any plant model high complexity and severe nonlinearity of power systems are introduced and two-Machine Five -Bus Power system stabilization problem is formulated. The simulation results demonstrate the effectiveness and application possibility of the FARMA controller to the control problem of high order and nonlinear plants.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.6
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• pp.509-512
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• 2012

This paper presents the state feedback control design for discrete time nonlinear systems where there exists a time delay in input. It is shown that under some boundedness condition, the time delay nonlinear systems can be transformed into the time delay linear systems with time varying parameters. Sufficient conditions for existence of stabilizing state feedback controller are characterized by linear matrix inequalities. Finally, an illustrative example is given in order to show the effectiveness of our design method.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.490-495
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• 1992

A neural model predictive control strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrained nonlinear optimization approach using successive quadratic programming cooperates with neural identification network is used to generate the optimum control law for the complicate continuous/batch chemical reactor systems that have inherent nonlinear dynamics. Based on our approach, we developed a neural model predictive controller(NMPC) which shows excellent performances on nonlinear, model-plant mismatch cases of chemical reactor systems.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.9
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• pp.651-657
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• 2003

Linearization is one of the most successful approaches nonlinear system control. The objective of this paper is to survey the recent results in linearization theory. It is hoped to be useful in understanding various linearization problems and challenging unsolved problems.

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.501-507
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• 2007

This paper presents a new algorithm for the closed-loop $H_{\infty}$ control of a class of singularly perturbed nonlinear systems with an exogenous disturbance, using the successive Galerkin approximation (SGA). The singularly perturbed nonlinear system is decomposed into two subsystems of a slow-time scale and a fast-time scale in the spirit of the general theory of singular perturbation. Two $H_{\infty}$ control laws are obtained to each subsystem by using the SGA method. The composite control law that consists of two $H_{\infty}$ control laws of each subsystem is designed. One of the purposes of this paper is to design the closed-loop $H_{\infty}$ composite control law for the singularly perturbed nonlinear systems via the SGA method. The other is to reduce the computational complexity when the SGA method is applied to the high order systems.

• Journal of the Korean Mathematical Society
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• v.50 no.1
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• pp.17-39
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• 2013

The study of nonlinear discrete-time control dynamical systems with disturbance is an important topic in control theory. In this paper, we concentrate our efforts to multiple valued iterative dynamical systems, which model the nonlinear discrete-time control dynamical systems with disturbance. After establishing the validity of such modeling, we study the invariant set theory of the multiple valued iterative dynamical systems, including the controllability/reachablity problems of the maximal invariant sets.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.583-595
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• 2008

In this paper, we introduce a fuzzy nonlinear feedback approach to the control of a class of chaotic dynamical systems. The fuzzy Parallel Distributed Compensation with Reduced Rule Base approach (PDC_RRB) is proposed. The design procedure is conceptually simple and considered to a nonlinear optimal and robust control problem due to the nonlinear nature of the Takagi-Sugeno (TS) fuzzy system. Simulation results are provided to show the effictiveness of the proposed methodology.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.27.2-27
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• 2001

This paper proposes a receding horizon predictive control (RHPC) for nonlinear time-delay systems. The control law is obtained by minimizing finite horizon cost with a terminal weighting functional. An inequality condition on the terminal weighting functional is presented, under which the closed-loop stability of RHPC is guaranteed, A special class of nonlinear time-delay systems is introduced and a systematic method to find a terminal weighting functional satisfying the proposed inequality condition is given for these systems. Through a simulation example, it is demonstrated that the proposed RHPC has the guaranteed closed-loop stability for nonlinear time-delay systems.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.159-162
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• 1997

This paper describes robust stabilization of nonlinear single-input uncertain systems without matching conditions. We consider nonlinear systems with a vector of unknown constant parameters perturbed about a known value. The approach utilizes the generalized controller canonical form to lump the unmatched uncertainties recursively into the matched ones. This can be achieved via nonlinear coordinate transformations which depend not only on the states of the nonlinear system but also on the control input. Then the dynamic robust control law is derived and the stability result is also presented.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.118-124
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• 2004

In this paper, we present a indirect adaptive control method using a wavelet neural network (WNN) for the control of chaotic nonlinear systems without precise mathematical models. The proposed indirect adaptive control method includes the off-line identification and on-line control procedure for chaotic nonlinear systems. In the off-line identification procedure, the WNN based identification model identifies the chaotic nonlinear system by using the serial-parallel identification structure and is trained by the gradient-descent method. And, in the on-line control procedure, a WNN controller is designed by using the off-line identification model and is trained by the error back-propagation algorithm. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with applications to the chaotic nonlinear systems.