• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.377-380
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• 1992

This paper proposes a novel direct adaptive pole placement control algorithm which can be applied to continuous time nonminimum phase systems. The algorithm is based on Lyapunov's direct method. By introducing an auxiliary signal, a minimal error model is constructed in state space. Using the error model an estimation law is obtained via Lyapunov's second stability theorem. The global stability of the overall system is established.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.51-54
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• 1990

A parametrization for a linear system is presented to design a direct model reference adaptive pole placement controler. This parametrized model is one of the structured nonminimal models. The exponentially weighted least-squres algorithm is employed to estimate the control parameters. The direct adaptive controller has the exponential weighting properties by the proposed method of selecting the characteristic polynomials of the sensitivity function filters in connection with the reference models.

• Information and Communications Magazine
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• v.21 no.6
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• pp.89-101
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• 2004

최근 저전력의 무선통신과 적응형 자가조직(Self-organization)능력을 가진 초소형 마이크로센서의 하드웨어 기술 발전으로 다양한 기능의 센서를 이용한 무선 센서 네트워크의 구축이 가능하게 되었다. 무선 센서 네트워크는 현실세계의 여러 이벤트를 감지하는 센싱 작업을 기반으로 주로 과학용이나 군사용으로 많이 사용되고있다. 즉, 직접 접근이 용이하지 않은 전장에서의 적군감시, 열악한 환경을 모니터링 하는 지진감지, 교통의 감시와 제어를 위한 지능형 교통통제, 장기간의 관찰이 요구되는 생태감시 등 그 응용범위가 다양하게 확장되고 있다. (중략)

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.4
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• pp.445-449
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• 1986

This paper presents a direct adaptive control scheme for nonminimum phase systems of which controller parameters are estimated from the least-squares algorithm, and some additional auxiliadry parameters are obtianed from the proposed polynomial identity equation. Integral action is incorporated into the adaptive controller to eliminate the steady-state error, and to satisfy a condition of the unique solution for the polynomial identity as well.

• Nuclear Engineering and Technology
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• v.27 no.6
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• pp.833-844
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• 1995

In self-tuning predictive control algorithm for steam generator is presented. The control algorithm is derived by suitably modifying the generalized predictive control algorithm. The main feature of the unposed method relies on considering the measurable disturbance and a simple adaptive scheme for obtaining the controller gain when the parameters of the plant are unknown. This feature makes the proposed approach particularly appealing for water level control of steam generator when measurable disturbance is used. In order to evaluate the performance of the proposed algorithm, computer simulations are done for an PWR steam generator model. Simulation result show satisfactory performances against load variations and steam flow rate estimation errors. It can be also observed that the proposed algorithm exhibit better responses than a conventional PI controller.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.219-222
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• 1997

Due to the unpredictability and irregularity, the behaviors of chaotic systems are considered as undesirable phenomena to be avoided or controlled. Thus in this paper, to control systems showing chaotic behaviors, a direct adaptive control method using a radial basis function network (RBFN) as an excellent alternative of multi-layered feed-forward networks is presented. Compared with an indirect scheme, a direct one does not need the estimation of the controlled process and gives fast control effects. Through simulations on the two representative continuous-time chaotic systems, Duffing and Lorenz systems, validity of the proposed control scheme is shown.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.7
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• pp.533-538
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• 2002

We propose a direct adaptive controller for a class of nonlinearly parametrized systems. A normalized output error is added to the previous control law to construct the proposed adaptive controller. The transient performance of the resulting closed-loop system can be guaranteed by suitably choosing a novel kind of Lyapunov function while the tracking performance of the adaptive control system is improved using the proposed algorithm. Finally, the effectiveness of the proposed algorithm is illustrated with computer simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.2
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• pp.99-106
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• 2003

A new reconfigurable model following flight control method based on direct adaptive scheme is presented. Using the timescale separation principle, both the inner-loop and the outer-loop states are controlled simultaneously. For the timescale separation assumption to be satisfied, the inner-loop model dynamics is set to be fast whereas the outer-loop model dynamics is set to be relatively slow. The stability and convergence of the proposed control law is proved by Lyapunov theorem. One of the merits of the proposed reconfigurable controller is that the FDI process and the persistent input excitation are not necessary, which is suitable for the flight control system. To evaluate the reconfiguration performance of the proposed control method, numerical simulation is performed using six degree-of-freedom nonlinear dynamics.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.4
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• pp.264-274
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• 2003

This paper presents a direct 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, noises and time delays. The self-tuning controller with a PID structure is a combination of the simple structure of a PID controller and the characteristics of a self-tuning controller that can adapt to changes in the environment. The self-tuning control effect is achieved through the RLS (recursive least square) algorithm at the parameter estimation stage as well as through the Robbins-Monro algorithm at the stage of optimizing the design parameter of the controller. The neural network control effect which compensates for nonlinear factor is obtained from the learning algorithm which the learning error between the filtered reference and the auxiliary output of plant becomes zero. Computer simulation has shown that the proposed method works effectively on the nonlinear nonminimum phase system with time delays and changed system parameter.

• Journal of IKEEE
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• v.23 no.2
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• pp.716-721
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• 2019

A novel switching differentiator-based backstepping controller for uncertain pure-feedback nonlinear systems is proposed. Using asymptotically convergent switching differentiator, time-derivatives of the virtual controls are directly estimated in every backstepping design steps. As a result, the control law has an extremely simple form and asymptotical stability of the tracking error is guaranteed regardless of parametric or unstructured uncertainties and unmatched disturbances in the considered system. It is required no universal approximators such as neural networks or fuzzy logic systems that are adaptively tuned online to cope with system uncertainties. Simulation results show the simplicity and performance of the proposed controller.