• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.1728-1729
• /
• 2007

This paper presents an adaptive neural dynamic surface control (DSC) approach with $H_{\infty}$ tracking performance for a full dynamics of a nonlinear flight system. It is assumed in this paper that model uncertainties such as structured and unstrutured uncertainties and external disturbances influence the nonlinear aircraft model. In our control system, self recurrent wavelet neural networks (SRWNNs) are used to compensate model uncertainties of the nonlinear flight system, and an adaptive DSC technique is extended for disturbance attenuation of the nonlinear flight system. From Lyapunov stability theorem, it is shown that $H_{\infty}$ performance from external disturbances can be obtained. Finally, we perform the simulation for the nonlinear six-degree-of-freedom F-16 aircraft model to confirm the effectiveness of the proposed control system.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.1716-1717
• /
• 2007

In this paper, a second order fuzzy sliding mode control that combines with a adaptive self-regulating technique is proposed for a nonlinear system with unknown dynamics. The chattering effect that is a representative disadvantage of the sliding mode control is avoided by using the second order sliding mode control instead of the first order sliding mode control. The proposed sub-controller is composed of the equivalent control that is approximated by an online rule regulation sheme and the hitting control that is used to constrain the states of the sub-system to maintain on the sub-sliding surface and used to guarantee the system robustness. Simulation results are presented to show the effectiveness of the proposed controller

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.44 no.6
• /
• pp.1-10
• /
• 2007

This paper presents a direct nonlinear multivariable self-tuning PID controller using neural network which adapts to the changing parameters of the nonlinear multivariable system with noises and time delays. The nonlinear multivariable system is divided linear part and nonlinear part. The linear controller are used the self-tuning PID controller that can combine the simple structure of a PID controllers with the characteristics of a self-tuning controller, which can adapt to changes in the environment. The linear controller parameters are obtained by the recursive least square. And the nonlinear controller parameters are achieved the through the Back-propagation neural network. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation results are presented to adapt the nonlinear multivariable system with noises and time delays and with changed system parameter after a constant time. The proposed PID type nonlinear multivariable self-tuning method using neural network is effective compared with the conventional direct multivariable adaptive controller using neural network.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.39 no.6
• /
• pp.1-8
• /
• 2002

This paper presents a generalized minimum-variance self-tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior and time delays. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation is done to adapt the nonlinear nonminimum phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct adaptive controller using neural network.

• Proceedings of the KIPE Conference
• /
• 2005.07a
• /
• pp.697-700
• /
• 2005

An adaptive fuzzy sliding-mode controller (SMC) for uncertain or ill-defined single-input single-output (SISO) nonaffine nonlinear systems is proposed. By using the universal approximation property of the fuzzy logic system (FLS), it is tuned on-line to cancel the unknown system nonlinearity. We adopt a self-structuring FLS to guarantee global stability of the closed-loop system rather than semi=global boundedness. The control and adaptive laws are derived so that the estimated fuzzy parameters are bounded and the sliding condition is satisfied.

• Proceedings of the KIEE Conference
• /
• 1991.07a
• /
• pp.375-378
• /
• 1991

This paper presents a design method of synchronous generator excitation controller using self-tuning PID algorithm. Controller parameter is determined by using adaptive control theory in order to maintain optimal operation of generator under the various operating conditions. To determine the optimal parameter of controller. minimum variance algorithm using the recursive leastsquare(RLS) indentification method is adopted and the difference between the speed deviation with weighted factor and voltage deviation is used as the input signal of adaptive controller, which provides good damping and conversion characteristics. The results tested on a single machine infinite bus system verify that the proposed controller has better dynamic performances than conventional controller.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.684-688
• /
• 1996

In this paper, to overcome drawbacks of variable structure control system a self-tuning fuzzy sliding mode control algorithm using gradient descent method is proposed. The proposed method has the characteristics which are viewed in conventional VSC, e.g. insensitivity to a class of disturbance, parameter variations and uncertainties in the sliding mode. To demonstrate its performance, the proposed control algorithm is applied to a one-degree of freedom robot arm. The results show that both alleviation of chattering and performance are achieved.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.11a
• /
• pp.63-66
• /
• 1997

The rule bases self organizing controller(SOC) has one of its main advantages in the fact that there is no need to have a mathematical description of the system to be controlled. In this controller, the rules are linguistics statements expressed mathematically through the concepts of fuzzy sets and correspond to the actions a human operator would take when controlling a given process. With this controller, we have performed to sight stabilization system, and we realize that it needs a scale factor tuning. The self tuning controller(STC) uses an instantaneous system fuzzy performance which can give an inspection to the scale factor. Therefore, the STC can compensate the scale factor when it is not adequately tuned. With this trial, we shows that STC can give a good transient characteristics in the nonlinearity which imposed basically in the conventional servo system.

• Proceedings of the Korean Information Science Society Conference
• /
• 2008.06d
• /
• pp.322-326
• /
• 2008

시스템의 규모가 대형화되어 감에 '시스템의 시스템'의 형태로써 대규모 사이즈의 프로그램 다양한 목적을 가진 사용자들, 대규모 저장 데이터양과 처리, 소프트웨어 컴포넌트간의 복잡한 연결상과 상호 의존성, 하드웨어의 다양성등을 포함하는 초대형 규모로 발전하고 있다. 또한 유, 무선 인터넷의 보편화와 소형기기들의 인터넷화 및 기존 서비스들의 개방화가 진행됨에 따라 새로운 독자적인 서비스를 만들기 보다는 SOA기반의 기존 시스템을 통합하여 새로운 서비스를 만드는 시도가 일어나고 있다. 최근 진행되고 있는 국가 및 산업의 대형 프로젝트들은 이러한 흐름에 따라 IT기술을 융합한 소프트웨어 기반의 초 대형 시스템 (Ultra Large Scale System)을 필요로 하고 있다. 이에 본 논문에서는 이러한 정보와 시스템의 대규모화에 대한 즉각적인 대처를 할 수 있는 Ultra Large Scale 시스템의 자율적응형 (Self-Adaptive) 기술 연구를 위하여 Self-Healing, Self-Integrating, Self-Orchestrating, Self-Managing, Self-Adaptring의 5가지 관점에서의 연구를 제안한다. 본 논문에서 제안하고 있는 연구의 파급 효과를 극대화 할 수 있는 영역은 e-Biz 시스템 u-city 시스템, USN 기반 물류 시스템 자동차 및 조선 사업의 IT융합 등의 대규모 시스템이 될 것이다.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.2 no.1
• /
• pp.65-69
• /
• 2002

The main drawback of “classical”fuzzy systems is the inability to design and maintain their database. To overcome this disadvantage many types of extensions adding the adaptivity property to those systems were designed. This paper deals with one of them a new hybrid adaptation structure, called gradient-incremental adaptive fuzzy controller connecting gradient-descent methods with the so-called self-organizing fuzzy logic controller designed by Procyk and Mamdani. The aim is to incorporate the advantages of both Principles. This controller was implemented and tested on the system of LEGO robots. The results and comparison to a ‘classical’(non-adaptive) fuzzy controller designed by a human operator are also shown here.