• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.7 s.178
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• pp.1689-1696
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• 2000

This paper proposes a new adaptive control scheme for flexible-link robots. A model-based nonlinear control scheme is designed based on a V-shape Lyapunov function, and then the nonlinear control i s extended to a model-based adaptive control to cope with parametric uncertainties in the dynamic model. The proposed control guarantees the global exponential or global asymptotic stability of the overall control system with all internal signals bounded. The effectiveness of the proposed control is shown by computer simulation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.145-150
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• 2005

We proposed the indirect adaptive fuzzy model based sliding mode controller to control nonaffine nonlinear systems. Takagi-Sugano fuzzy system is used to represent the nonaffine nonlinear system and then inverted to design the controller at each sampling time. Also sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. The proposed controller and adaptive laws guarantee that the closed-loop system is stable in the sense of Lyapunov and the output tracks a desired trajectory asymptotically.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.113-117
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• 2002

PID controllers, which have been widely used in industry, have a simple structure and robustness to modeling error. But it is difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose a nonlinear adaptive PR controller based on an Immune feedback mechanism and a gradient descent teaming. This algorithm has a simple structure and robustness to system parameters variation. To verify performances of the proposed nonlinear adaptive PID controller, the speed control of nonlinear DC motor Is peformed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation

• Proceedings of the KIEE Conference
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• 2008.11b
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• pp.65-68
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• 2008

This paper presents the new adaptive optimal scheme for the nonlinear systems, which is based on the Picard's iterative approximation and Fast Walsh transform. It is well known that the Walsh function approach method is very difficult to apply for the analysis and optimal control of nonlinear systems. However, these problems can be easily solved by the improvement of the previous adaptive optimal scheme. The proposed method is easily applicable to the analysis and optimal control of nonlinear systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.88-95
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• 2006

PID controllers, which have been widely used in industry, have a simple structure and robustness to modeling error. But They we difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose a nonlinear adaptive PID controller based on a cell-mediated immune response and a gradient descent learning. This algorithm has a simple structure and robustness to system parameters variation. To verify performances of the proposed nonlinear adaptive PID controller, the speed control of nonlinear DC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation.

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

• International Journal of Aeronautical and Space Sciences
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• v.11 no.4
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• pp.285-302
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• 2010

Active aeroelastic control is an emerging technology aimed at providing solutions to structural systems that under the action of aerodynamic loads are prone to instability and catastrophic failures, and to oscillations that can yield structural failure by fatigue. The purpose of the aeroelastic control among others is to alleviate and even suppress the vibrations appearing in the flight vehicle subcritical flight regimes, to expand its flight envelope by increasing the flutter speed, and to enhance the post-flutter behavior usually characterized by the presence of limit cycle oscillations. Recently adaptive and robust control strategies have demonstrated their superiority to classical feedback strategies. This review paper discusses the latest development on the topic by the authors. First, the available control techniques with focus on adaptive control schemes are reviewed, then the attention is focused on the advanced single-input and multi-input multi-output adaptive feedback control strategies developed for lifting surfaces operating at subsonic and supersonic flight speeds. A number of concepts involving various adaptive control methodologies, as well as results obtained with such controls are presented. Emphasis is placed on theoretical and numerical results obtained with the various control strategies.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.457-460
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• 1996

An adaptive pH control is developed to manipulate the nonlinearities and time-varying properties of pH systems. In this research, we estimate two adjustable parameters by using the recursive least squares method and a nonlinear PI controller is used to control pH systems based on the estimated two parameters.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.236-241
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• 2007

This paper presents an adaptive controller using chaotic dynamic neural networks(CDNN) for nonlinear dynamic system. A new dynamic backpropagation learning method of the proposed chaotic dynamic neural networks is developed for efficient learning, and this learning method includes the convergence for improving the stability of chaotic neural networks. The proposed CDNN is applied to the system identification of chaotic system and the adaptive controller. The simulation results show good performances in the identification of Lorenz equation and the adaptive control of nonlinear system, since the CDNN has the fast learning characteristics and the robust adaptability to nonlinear dynamic system.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.10
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• pp.1002-1007
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• 2014

This paper deals with the adaptive backstepping hovering control for a quadrotor with model parameter uncertainties. In this paper, the backstepping based technique is utilized to design a nonlinear adaptive controller which can compensate for the motor thrust factor and the drag coefficient of a quadrotor. First, the quadrotor nonlinear dynamics is derived using Newton-Euler formulation. In particular, we use the ${\pi}/4$ shifted coordinate for x- and y-axis of a quadrotor. Second, an adaptive backstepping based attitude and altitude tracking control method is presented. The system stability and the convergence of tracking errors are proven using the Lyapunov stability theory. Finally, the simulation results are given to verify the effectiveness of the proposed control method.