• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.233-240
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• 2006

This paper presents the robust control method using a self recurrent wavelet neural network (SRWNN) via adaptive backstepping design technique for stable walking of biped robots with unknown model uncertainties. The SRWNN, which has the properties such as fast convergence and simple structure, is used as the uncertainty observer of the biped robots. The adaptation laws for weights of the SRWNN and reconstruction error compensator are induced from the Lyapunov stability theorem, which are used for on-line controlling biped robots. Computer simulations of a five-link biped robot with unknown model uncertainties verify the validity of the proposed control system.

• Journal of Astronomy and Space Sciences
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• v.23 no.4
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• pp.405-414
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• 2006

Satellite formation flying has become a critical issue in the aerospace engineering because it is considered as an enabling technology for many space missions. Thus, many nonlinear control theories have been developed for the tracking problem of satellite formation flying, which include full-nonlinear dynamics, external disturbances and parameter uncertainty. In this study, nonlinear adaptive control law is developed using an adaptive backstepping technique to solve the relative position tracking problem of the satellite formation flying in the presence of mass uncertainty and the bounded external disturbance. Simulation studies are included to demonstrate the proposed controller performance. The proposed controller is shown to guarantee the system stability against the external bounded disturbances in the presence of mass uncertainty.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.2
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• pp.127-134
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• 2008

In this paper, we propose a robust adaptive backstepping control of induction motors with uncertainties using nonlinear disturbance observer(NDO). The proposed NDO is applied to estimate the time-varying lumped uncertainty which are derived from unknown motor parameters and load torque, but NDO error does not converge to zero since the derivate of lumped uncertainty is not zero. Then the fuzzy neural network(FNN) is presented to estimate the NDO error such that the rotor speed to converge to a small neighborhood of the desired trajectory. Rotor flux and inverse time constant are estimated by the sliding mode adaptive flux observer. Simulation results are provided to verify the effectiveness of the proposed approach.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2087-2088
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• 2001

In this paper we presents a speed controller for permanent magnet synchronous motor using adaptive backstepping technique. The adaptive backstepping technique takes system nonlinearity into account in the control system design stage. The proposed control and adaptive law is proved to be asymptotically stable by the Lyapunov stability theory.

• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.2
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• pp.347-360
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• 2014

The auto-berthing of a ship requires excellent control for safe accomplishment. Crabbing, which is the pure sway motion of a ship without surge velocity, can be used for this purpose. Crabbing is induced by a peculiar operation procedure known as the push-pull mode. When a ship is in the push-pull mode, an interacting force is induced by complex turbulent flow around the ship generated by the propellers and side thrusters. In this paper, three degrees of freedom equations of the motions of crabbing are derived. The equations are used to apply the adaptive backstepping control method to the auto-berthing controller of a cruise ship. The controller is capable of handling the system non-linearity and uncertainty of the berthing process. A control allocation algorithm for a ship equipped with two propellers and two side thrusters is also developed, the performance of which is validated by simulation of auto-berthing.

• Journal of Ocean Engineering and Technology
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• v.32 no.2
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• pp.131-137
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• 2018

In this paper, we propose an adaptive backstepping controller to control the exact position and orientation of a remotely operated underwater vehicle with parametric model uncertainty. To further improve the angular velocity control precision of each thruster, a phase locked loop (PLL) controller has been added to the backstepping controller. A comparison of two backstepping controllers with and without the PLL control loop has been performed using simulations and experiments. The test results showed that the tracking performance could be improved by using the PLL control loop in the proposed adaptive backstepping controller.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.3
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• pp.119-128
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• 2005

In this paper, an robust adaptive backstepping controller is proposed for the chaos system with disturbances. This controller will be applicable to the chaos system of strict-feedback form and utilize the saturation function for decreasing the effect of disturbances derived from unmodelled dynamics and external noise. It shows that backstepping algorithm can be used to solve the problems of nonlinear system very well and robust controller can be designed without the variation of adaptive law. Simulation results are provided to demonstrate the effectiveness of the proposed controller.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.4
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• pp.329-339
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• 2003

A neural network based adaptive controller design method is proposed for reconfigurable flight control systems in the presence of variations in aerodynamic coefficients or control effectiveness decrease caused by control surface damage. The neural network based adaptive nonlinear controller is developed by making use of the backstepping technique for command following of the angle of attack, sideslip angle, and bank angle. On-line teaming neural networks are implemented to guarantee reconfigurability and robustness to the uncertainties caused by aerodynamic coefficients variations. The main feature of the proposed controller is that the adaptive controller is designed with assumption that not any of the nonlinear functions of the system is known accurately, whereas most of the previous works assume that only some of the nonlinear functions are unknown. Neural networks loam through the weight update rules that are derived from the Lyapunov control theory. The closed-loop stability of the error states is also investigated according to the Lyapunov theory. A nonlinear dynamic model of an F-16 aircraft is used to demonstrate the effectiveness of the proposed control law.

• Kyungpook Mathematical Journal
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• v.55 no.3
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• pp.563-586
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• 2015

In this research work, a seven-term 3-D novel jerk chaotic system with two quadratic nonlinearities has been proposed. The basic qualitative properties of the novel jerk chaotic system have been described in detail. Next, an adaptive backstepping controller is designed to stabilize the novel jerk chaotic system with two unknown parameters. Moreover, an adaptive backstepping controller is designed to achieve complete chaos synchronization of the identical novel jerk chaotic systems with two unknown parameters. MATLAB simulations have been shown in detail to illustrate all the main results developed for the 3-D novel jerk chaotic system.

• Proceedings of the KIPE Conference
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• 2010.07a
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• pp.606-607
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• 2010

본 논문은 CVCF 인버터 출력전압을 정현파로 제어하는 기법을 제안한다. 왜곡이 없는 깨끗한 정현파 출력전압을 요구하는 AVR이나 UPS에 적용이 가능하다. 변동 부하나 비선형 부하에 대해서도 출력을 안정되게 제어할 수 있는 장점이 있다. Backstepping 제어 기법을 이용하여 인버터 출력전류를 가상 제어변수로 선정하고 콘덴서 양단전압이 지령값을 추종하도록 가상 제어변수를 조절하고 이를 위해 인버터 출력전압인 입력 제어변수를 조절한다. 출력 필터의 L값의 변동을 고려하여 Adaptive parameter 보상 알고리즘을 적용하여 정밀한 Parameter값을 요구하는 Backstepping 기법의 단점을 보완하였다. 제안한 알고리즘의 타당성을 시뮬레이션을 통해 검증하였다.