• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.2
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• pp.28-36
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• 1996

By utilizing the interinsic structure of the underlying continous-time nonlinear system, one can design an approximate sampled-data observer improved with respect ot the sampling-time for the systems. In this paper, we characterize the conditions for the solvability of the improved approximate sampled-data nonlinear observer design problem. In particular, it is shown that when the dimension of the state space is two, the nonlinear systems for which it is possible ot desing 3rd or higher order approximate sampled-data nonlinear observer are locally state-equivalent to an observable bilinear system. The practical implication is that seeking higher order approximate sampled-data nonlinear observer for nonlinear systems is very restricted.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.76.5-76
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• 2002

In this paper, we provide a Nash solution to predictive control problem for nonminimum phase singular nonlinear systems. Until now, there is no result on predictive control problem for this class of nonlinear systems. Chen's recent work considered predictive control problem for a class of nonlinear systems with ill-defined relative degree. Since his work is not a result considered in the feedback linearization framework, there is no a result on singular probem in his paper. In contrast to the existing predictive control result, our work considers two main obstacles (singularity and nonminimum phase) in the feedback linearization framework. For a generally formu...

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.939-942
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• 1989

Most of systems are included nonlinear characteristics in practice. One might be faced with difficulties when problems of nonlinear systems are solved. In this paper we present a formal linearization method of nonlinear systems by using the trigonometric Fourier expansion on the state space considering easy inversion. An error bound, an application, and a compensation of this method are also investigated.

• 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.

• 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.14 no.1
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• pp.42-48
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• 2008

This paper presents an observer design method for a special class of multi input multi output(MIMO) nonlinear systems. First, we characterize the class of MIMO nonlinear systems with a block triangular structure. Also, the observability matrices for SISO nonlinear systems are extended to MIMO systems. By using the generalized observability matrices, it is shown that under the boundedness conditions of system state and input, the proposed observer guarantees the local exponential stability of error dynamics. Finally, its application to induction motor is given to verify the proposed method.

• Smart Structures and Systems
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• v.15 no.2
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• pp.409-424
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• 2015

Structural nonlinearity is a common phenomenon encountered in engineering structures under severe dynamic loading. It is necessary to localize and identify structural nonlinearities using structural dynamic measurements for damage detection and performance evaluation of structures. However, identification of nonlinear structural systems is a difficult task, especially when proper mathematical models for structural nonlinear behaviors are not available. In prior studies on nonparametric identification of nonlinear structures, the locations of structural nonlinearities are usually assumed known and all structural responses are measured. In this paper, an identification algorithm is proposed for locating and identifying model-free structural nonlinearities and systems using incomplete measurements of structural responses. First, equivalent linear structural systems are established and identified by the extended Kalman filter (EKF). The locations of structural nonlinearities are identified. Then, the model-free structural nonlinear restoring forces are approximated by power series polynomial models. The unscented Kalman filter (UKF) is utilized to identify structural nonlinear restoring forces and structural systems. Both numerical simulation examples and experimental test of a multi-story shear building with a MR damper are used to validate the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.239-242
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• 1996

A model following control system(MFCS) can give general output signals following desired ones. In previous studies, a method of nonlinear MFCS was proposed by S.Okubo[1]. In this paper, the method of nonlinear MFCS will be extended to discrete time nonlinear systems. It is easy to extend the method to discrete time systems. But in the case .gamma.=1 discrete time systems, the proof becomes difficult, because the transfer function from f(v(k)) to v(k) can't be a positive real function. In this case, to ensure that internal states are stable, a new criterion is proposed.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.15-22
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• 2004

Some recent advances in stability and optimality for the nonlinear receding horizon control (NRHC) or the nonlinear model predictive control (NMPC) are assessed. The NRHCs with terminal conditions are surveyed in terms of a terminal state equality constraint, a terminal cost, and a terminal constraint set. Other NRHCs without terminal conditions are surveyed in terms of a control Lyapunov function (CLF) and cost monotonicity. Additional approaches such as output feedback, fuzzy, and neural network are introduced. This paper excludes the results for linear receding horizon controls and concentrates only on the analytical results of NRHCs, not including applications of NRHCs. Stability and optimality are focused on rather than robustness.