• Proceedings of the KIPE Conference
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• 2016.07a
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• pp.519-520
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• 2016

BLSRM is a nonlinear, strong coupling and multi-variable system. The conventional control method is vulnerable to uncertain factors such as the load disturbance and satellite parameters change. It is difficult to obtain satisfactory control effect. Basing on a 8/10 BLSRM, whose suspending force control is separated with the torque control, this paper presents adaptive fuzzy PID controller for levitation control, which apply the fuzzy logic control to the conventional PID controller for parameters self-tuning. Both fuzzy and parameters of PID controller are self-tuning on-line, which improve the performance of controller. Finally, simulation and experimental results show the performance of the proposed method.

• International Journal of Aeronautical and Space Sciences
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• v.12 no.2
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• pp.179-189
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• 2011

In this paper, robust adaptive control design problem is addressed for a class of parametrically uncertain aeroelastic systems. A full-state robust adaptive controller was designed to suppress aeroelastic vibrations of a nonlinear wing section. The design used leading and trailing edge control actuations. The full state feedback (FSFB) control yielded a global uniformly ultimately bounded result for two-axis vibration suppression. The pitching and plunging displacements were measurable; however, the pitching and plunging rates were not measurable. Thus, a high gain observer was used to modify the FSFB control design to become an output feedback (OFB) design while the stability analysis for the OFB control law was presented. Simulation results demonstrate the efficacy of the multi-input multi-output control toward suppressing aeroelastic vibrations and limit cycle oscillations occurring in pre- and post-flutter velocity regimes.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.183-184
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• 2008

본 논문에서는 불확실성을 가지는 비선형 시스템에 대한 강인 유한 시간 안정화 문제를 고려한다. 제안된 설계기법은 역진기법과 추가된 다이나믹스를 이용한 다이나믹 지수 보정법에 기반을 두고 있으면, 이는 전체 시스템의 유한시간 안정화와 제어기의 유한함을 보장한다.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1955-1959
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• 2005

This paper presents a systematic analysis and a simple design of a robust adaptive control law for a class of non linear systems with modeling errors and a time-delay input. The theory for designing a robust adaptive control law based on input- output feedback linearization of non linear systems with uncertainties and a time-delay in the manipulated input by the approach of parameterized state feedback control is presented. The main advantage of this method is that the parameterized state feedback control law can effectively suppress the effect of the most parts of nonlinearities, including system uncertainties and time-delay input in the pp-coupling perturbation form and the relative order of non linear systems is not limited.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.164-167
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• 2006

본 논문에서는 불확실한 비선형 시스템에 대한 적응 퍼지 슬라이딩 모드 제어기를 설계한다. 불확실한 비선형 시스템에서 발생할 수 있는 파라미터의 변화를 대처하기 위해서 적응 퍼지 이론을 이용하였고, 외란으로 인한 불확실성을 슬라이딩 모드의 제어기를 통해서 해결하였다. 또한 퍼지 튜닝을 통해 슬라이딩 조건을 가변화함으로써 기존의 슬라이딩 모드 제어기에 비해 빠르고 정확하게 추종 가능하도록 제어기의 성능을 향상시킨다. 제안하는 제어기는 정확한 동역학 모델의 구현이 어렵고 복잡한 비선형 시스템에 외란 특성이 우수한 슬라이딩 모드와 실제 시스템을 표현하는 범용 근사자로 유용성이 입증된 퍼지 시스템을 이용하여 간단하고 쉽게 제어할 수 있도록 하였다. Lyapunov이론을 통하여 전역적인 안정화를 보이며, 마지막으로 역진자 시스템에 적용하여 제안된 제어기의 성능을 검증한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.29-34
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• 2001

This paper is devoted to investigating direct adaptive neural control of nonlinear systems with uncertain or unknown dynamic models. In the direct adaptive neural networks control area, theoretical issues of the existing backpropagation-based adaptive neural networks control schemes. The major contribution is proposing the variable index control approach, which is of great significance in the control field, and applying it to derive new stable robust adaptive neural network control schemes. This new schemes possess inherent robustness to system model uncertainty, which is not required to satisfy any matching condition. To demonstrate the feasibility of the proposed leaning algorithms and direct adaptive neural networks control schemes, intensive computer simulations were conducted based on the flexible joint robot systems and functions.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.4
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• pp.255-261
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• 2000

This paper presents the results of the robust H(sub)$\infty$ FIR filtering for a class of nonlinear continuous time-varying systems subject to real norm-bounded parameter uncertainty and know Lipschitz nonlinearity under sampled measurements. We address the problem of designing filters, using sampled measurements, which guarantee a prescribed H(sub)$\infty$ performance in continuous time-varying context, irrespective of the parameter uncertainty and unknown initial states. The infinite horizon causal H(sub)$\infty$FIR filter are investigated using the finite moving horizon in terms of two Riccati equations with finite discrete jumps.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.9
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• pp.1722-1727
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• 1992

In this study, techniques of adaptive input-output linearizing control of a class of uncertain nonlinear system are investigated. It is shown through concepts of signal growth rates that bounded trackings yield by adaptive input-output linearizing control law using normalized estimator. The convergence characteristics are improved significantly by using the normalized estimator. Simple example is presented as illustration.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2283-2286
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• 2000

Sliding control is a powerful approach to controlling nonlinear and uncertain systems. Conventional sliding mode control suffer' from high control gain and chattering problem. also it needs mathematic! modeling equations for control systems. A Fuzzy controller is endowed with control rules and membership function that are constructed on the knowledge of expert, as like intuition and experience. but It is very difficult to obtain the exact values which are the membership function and consequent parameters. In this paper, without mathematical modeling equations, the plant parameters in sliding mode are estimated by the indirect adaptive fuzzy method. the proposed algorithm could analyze the system's stability and convergence behavior using Lyapunov theory. so sliding modes are reconstructed and decreased tracking error. moreover convergence time took a short. An example of inverted pendulum is given for demonstration of the robustness of proposed methodology.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2290-2292
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• 2000

Robustness characteristics to the modelling imprecision and some disturbances could be achieved in sliding mode control. However, there are drawbacks such as discontinuous control and chattering. Recently, many researches have been developing to solve such the problems. In sliding mode control, overall control input could be divided into two parts which are equivalent control input and sliding mode control input. Sliding mode control input is a function of the switching surfaces and can be designed with their linear combinations. In this paper, the sliding mode control input is designed by TSK fuzzy model. The proposed method gives the continuous sliding control input and reject the chattering phenomenon.