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      저자 : Warren E. Dixon (검색결과 3 건)
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        Autonomous Flight of the Rotorcraft-Based UAV Using RISE Feedback and NN Feedforward Terms

        Jongho Shin,Kim, H. Jin,Youdan Kim,Dixon, Warren E. IEEE 2012 IEEE transactions on control systems technology Vol.20 No.5

        <P>A position tracking control system is developed for a rotorcraft-based unmanned aerial vehicle (RUAV) using robust integral of the signum of the error (RISE) feedback and neural network (NN) feedforward terms. While the typical NN-based adaptive controller guarantees uniformly ultimately bounded stability, the proposed NN-based adaptive control system guarantees semi-global asymptotic tracking of the RUAV using the RISE feedback control. The developed control system consists of an inner-loop and outer-loop. The inner-loop control system determines the attitude of the RUAV based on an adaptive NN-based linear dynamic model inversion (LDI) method with the RISE feedback. The outer-loop control system generates the attitude reference corresponding to the given position, velocity, and heading references, and controls the altitude of the RUAV by the LDI method with the RISE feedback. The linear model for the LDI is obtained by a linearization of the nonlinear RUAV dynamics during hover flight. Asymptotic tracking of the attitude and altitude states is proven by a Lyapunov-based stability analysis, and a numerical simulation is performed on the nonlinear RUAV model to validate the effectiveness of the controller.</P>

      • Autonomous flight of the rotorcraft-based UAV using RISE feedback and NN feedforward terms

        Jongho Shin,H. Jin Kim,Youdan Kim,Warren E. Dixon 한국산업응용수학회 2013 한국산업응용수학회 학술대회 논문집 Vol.8 No.1

        A position tracking control system is developed for a rotorcraft-based unmanned aerial vehicle (RUAV) using robust integral of the signum of the error (RISE) feedback and neural network (NN) feedforward terms. While the typical NN-based adaptive controller guarantees uniformly ultimately bounded stability, the proposed NN-based adaptive control system guarantees semiglobal asymptotic tracking of the RUAV using the RISE feedback control. The developed control system consists of an inner-loop and outer-loop. The inner-loop control system determines the attitude of the RUAV based on an adaptive NN-based linear dynamic model inversion (LDI) method with the RISE feedback. The outer-loop control system generates the attitude reference corresponding to the given position, velocity, and heading references, and controls the altitude of the RUAV by the LDI method with the RISE feedback. The linear model for the LDI is obtained by a linearization of the nonlinear RUAV dynamics during hover flight. Asymptotic tracking of the attitude and altitude states is proven by a Lyapunov-based stability analysis, and a numerical simulation is performed on the nonlinear RUAV model to validate the effectiveness of the controller.

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        Range and Motion Estimation of a Monocular Camera Using Static and Moving Objects

        Chwa, Dongkyoung,Dani, Ashwin P.,Dixon, Warren E. Institute of Electrical and Electronics Engineers 2016 IEEE transactions on control systems technology Vol. No.

        <P>We propose a method of estimating the motion of a monocular camera looking at moving objects and their range. Unlike the previous studies where the camera and object motion should be constrained in estimating structure and motion (SaM) of moving objects, the proposed method do not require those constraints even though only a monocular camera is used. By first arranging the SaM dynamics in terms of the measurable states, we design robust nonlinear observers in a sequential way for both static (stationary) and dynamic (moving) objects. Through the combination of these estimates obtained by nonlinear observers, the reconstruction of the 3-D structure of the dynamic objects can be achieved using just 2-D images of a monocular camera. Simulations are performed in the case of changing camera and object velocities, such that the advantages of the proposed method can be clearly demonstrated.</P>

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