• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1182-1185
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• 1996

The objective of this paper is to control a nonlinear dynamical systems based on Self-Organized Distributed Networks (SODN). The learning with the SODN is fast and precise. Such properties are caused from the local learning mechanism Each local network learns only data in a subregion. Methods for indirect adaptive control of nonlinear systems using the SODN is presented. Through extensive simulation, the SODN is shown to be effective for adaptive control of nonlinear dynamic systems.

• International Journal of Control, Automation, and Systems
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• v.3 no.spc2
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• pp.296-301
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• 2005

As a new control strategy, the Nonlinear PID (NLPID) controller has been introduced successfully in power systems. The controller is free of planting model groundwork during the design procedure and is therefore able to be achieved quite simply. In this paper, a nonlinear PID controller used for a superconducting magnetic energy storage (SMES) unit connected to a power system is proposed. The purpose of designing such a controller is to improve the stability of the power system in a relatively wide operation range. The design procedure takes into account the active and reactive power cooperative control scheme as well as the simple structure so as to be more apt to practical utilization. Simulation is carried out to investigate the performance of the proposed controller in a high order nonlinear power system model under the. MATLAB environment. The results show satisfactory performance and good robustness of the controller. The feasibility of the controller is testified as well.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.73-76
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• 1997

This paper proposes the fuzzy logic-based fast gain scheduling(FFGS) controller for regulation problem in nonlinear systems. It utilizes which reflects the derivative information on the original scheduling variable in order to achieve better performance than the existents. Moreover, we apply the proposed control scheme to control active suspension systems with nonlinear components.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.3
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• pp.277-288
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• 2014

This article reviews various state estimation methods for nonlinear systems, particularly with a perspective of a process control engineer. Nonlinear state estimation methods can be classified into the following two categories: stochastic approaches and deterministic approaches. The current review compares the Bayesian approach, which is mainly a stochastic approach, and the MHE (Moving Horizon Estimation) approach, which is mainly a deterministic approach. Though both methods are reviewed, emphasis is given to the latter as it is particularly well-suited to highly nonlinear systems with slow sampling rates, which are common in chemical process applications. Recent developments in underlying theories and supporting numerical algorithms for MHE are reviewed. Thanks to these developments, applications to large-scale and complex chemical processes are beginning to show up but they are still limited at this point owing to the high numerical complexity of the method.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.385-391
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• 2014

This paper proposes fuzzy disturbance observer based multiple sliding surface control scheme for nonlinear systems with mismatched disturbance. In order to stabilize nonlinear systems with mismatched disturbance, a controller based on multiple sliding surface control scheme is designed. In addition, a fuzzy disturbance observer is used to estimate the disturbance. Using the fuzzy disturbance observer, "explosion of terms" problem and chattering problem were solved. The stability of the proposed control scheme is analyzed by Lyapunov stability theory. For the verification, we apply the proposed method to numerical examples and compare its result with that of the applied nonlinear disturbance observer based sliding mode control.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.137-140
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• 1999

A lot of researcher have proposed a method of kernel identifying nonlinear system by use of Wiener kernels[6-7] or Volterra kernel[5] and so on. In this research, the authors proposed a method of identifying Volterra kernels for nonlinear system by use of pseudorandom M-sequence in which a crosscorrelation function between input and output of a nonlinear system is taken[4]. we can be applied to an MISO nonlinear system or a system which depends on its input amplitude[2]. But, there exist many systems in which it is difficult to determine a Volterra kernel having the same time coordinate on the crosscorrelation function. In those cases, we have to estimate Volterra kernel by using its neighboring points[4]. In this paper, we propose a new method for not estimating but obtaining Volterra kernel having the same time coordinate using calculation between the neighboring points. Some numerical simulations show that this method is effective for obtaining higher order Volterra kernel of nonlinear control systems.

• International Journal of Control, Automation, and Systems
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• v.4 no.6
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• pp.756-762
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• 2006

Shape Memory Alloy(SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications such as aeronautics, surgical tools, robotics and so on. Although the conventional PID controller can be used with slow response systems, there has been limited success in precise motion control of SMA actuators, since the systems are disturbed by unknown factors beside their inherent nonlinear hysteresis and changes in the surrounding environment of the systems. This paper presents a new development of a SMA position control system by using a self-tuning fuzzy PID controller. This control algorithm is used by tuning the parameters of the PID controller thereby integrating fuzzy inference and producing a fuzzy adaptive PID controller, which can then be used to improve the control performance of nonlinear systems. The experimental results of position control of SMA actuators using conventional and self-tuning fuzzy PID controllers are both included in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.48.2-48
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• 2002

This paper presents an integrated sliding mode adaptive control scheme for general nonlinear uncertain systems, where structured uncertainty is assumed can be linearly parameterized and unstructured uncertainty is assumed be bounded by unknown constant A certain estimation scheme for this unknown constant is introduced to attenuate the unstructured uncertainty. Presented control scheme is shown to be stable and numerical expressions of bounds of all error signals are given, from which we can acquire some useful information about practical trade-off between tracking performance and parameter estimation property.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1136-1141
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• 2015

In this paper, we develop a sliding mode controller that uses a nonlinear sliding manifold for the permanent magnet synchronous motor. The proposed controller makes sure that both currents and velocity tracking error converge into equilibria. Nonlinear sliding manifold consists of current dynamics and nonlinear functions which are designed with velocity tracking error and its integrated term. The nonlinear functions are designed to guarantee that velocity tracking error converge into zero. The closed-loop stability is proven by Lyapunov theory. The effectiveness of proposed method is demonstrated by numerical simulation results.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.327-329
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• 2006

A controller is proposed for the robust adaptive backstepping control of a class of uncertain nonlinear systems using nonlinear disturbance observer (NDO). The NDO is applied to estimate the time-varying lumped disturbance in each step, but a disturbance observer error does not converge to zero since the derivative of lumped disturbance is not zero. Then the fuzzy neural network (FNN) is presented to estimate the disturbance observer error such that the outputs of the system are proved to converge to a small neighborhood of the desired trajectory. The proposed control scheme guarantees that all the signals in the closed-loop are semiglobally uniformly ultimately bounded on the basis of the Lyapunov theorem. Simulation results are presented to illustrate the effectiveness and the applicability of the approaches proposed.