The Experts below are selected from a list of 183525 Experts worldwide ranked by ideXlab platform

Ning Wang - One of the best experts on this subject based on the ideXlab platform.

  • hybrid finite Time Trajectory tracking control of a quadrotor
    Isa Transactions, 2019
    Co-Authors: Ning Wang, Qi Deng, Guangming Xie, Xinxiang Pan
    Abstract:

    Abstract In this paper, accurate Trajectory tracking control problem of a quadrotor with unknown dynamics and disturbances is addressed by devising a hybrid finite-Time control (HFTC) approach. An adaptive integral sliding mode (AISM) control law is proposed for altitude subsystem of the quadrotor, whereby underactuated characteristics can decoupled. Backstepping technique is further deployed to control the horizontal position subsystem. To exactly attenuate external disturbances, a finite-Time disturbance observer (FDO) combining with nonsingular terminal sliding mode (NTSM) control strategy is constructed for attitude subsystem, and thereby achieve finite-Time stability. Using the compounded control scheme, Trajectory tracking errors can be stabilized rapidly. Simulation results and comprehensive comparisons show that the proposed HFTC scheme has remarkably superior performance.

  • nonlinear disturbance observer based backstepping finite Time sliding mode tracking control of underwater vehicles with system uncertainties and external disturbances
    Nonlinear Dynamics, 2017
    Co-Authors: Siyuan Liu, Yancheng Liu, Ning Wang
    Abstract:

    In this paper, a nonlinear disturbance observer-based backstepping finite-Time sliding mode control scheme for Trajectory tracking of underwater vehicles subject to unknown system uncertainties and Time-varying external disturbances is proposed. To reduce the influence of the uncertainties and external disturbances, a nonlinear disturbance observer is developed without any acceleration measurements to identify the lumped disturbance term. Additionally, the finite-Time Trajectory tracking controller is designed by combining second-order sliding mode control and backstepping design technique with the nonlinear disturbance observer. The finite-Time convergence of motion tracking errors and the stability of the overall closed-loop control system are guaranteed by the Lyapunov approach. Besides, comprehensive simulation studies on Trajectory tracking control of underwater vehicles are provided to demonstrate the effectiveness and performance of the proposed control scheme.

  • adaptive robust finite Time Trajectory tracking control of fully actuated marine surface vehicles
    IEEE Transactions on Control Systems and Technology, 2016
    Co-Authors: Ning Wang, Chunjiang Qian, Jingchao Sun, Yancheng Liu
    Abstract:

    In this brief, an adaptive robust finite-Time tracking control (ARFTTC) scheme for Trajectory tracking of a fully actuated marine surface vehicle with unknown disturbances is proposed. A new finite-Time disturbance observer is incorporated into the proposed finite-Time tracking control (FTTC) structure that facilitates faster convergence and better robustness to disturbances. Hence, in the presence of unknown disturbances, the ARFTTC can cause tracking error to converge to zero in a finite Time. Simulation studies and comprehensive comparisons with conventional backstepping technique demonstrate remarkable performance and superiority of the ARFTTC in terms of both tracking accuracy and robustness.

Timothy D Barfoot - One of the best experts on this subject based on the ideXlab platform.

  • a white noise on jerk motion prior for continuous Time Trajectory estimation on se 3
    International Conference on Robotics and Automation, 2019
    Co-Authors: Tim Y Tang, David J Yoon, Timothy D Barfoot
    Abstract:

    Simultaneous Trajectory estimation and mapping (STEAM) offers an efficient approach to continuous-Time Trajectory estimation, by representing the Trajectory as a Gaussian process (GP). Previous formulations of the STEAM framework use a GP prior that assumes white-noise-on-acceleration, with the prior mean encouraging constant body-centric velocity. We show that such a prior cannot sufficiently represent Trajectory sections with nonzero acceleration, resulting in a bias to the posterior estimates. This letter derives a novel motion prior that assumes white-noise-on-jerk, where the prior mean encourages constant body-centric acceleration. With the new prior, we formulate a variation of STEAM that estimates the pose, body-centric velocity, and body-centric acceleration. By evaluating across several datasets, we show that the new prior greatly outperforms the white-noise-on-acceleration prior in terms of the solution accuracy.

  • a white noise on jerk motion prior for continuous Time Trajectory estimation on se 3
    arXiv: Robotics, 2018
    Co-Authors: Tim Y Tang, David J Yoon, Timothy D Barfoot
    Abstract:

    Simultaneous Trajectory estimation and mapping (STEAM) offers an efficient approach to continuous-Time Trajectory estimation, by representing the Trajectory as a Gaussian process (GP). Previous formulations of the STEAM framework use a GP prior that assumes white-noise-on-acceleration, with the prior mean encouraging constant body-centric velocity. We show that such a prior cannot sufficiently represent Trajectory sections with non-zero acceleration, resulting in a bias to the posterior estimates. This paper derives a novel motion prior that assumes white-noise-on-jerk, where the prior mean encourages constant body-centric acceleration. With the new prior, we formulate a variation of STEAM that estimates the pose, body-centric velocity, and body-centric acceleration. By evaluating across several datasets, we show that the new prior greatly outperforms the white-noise-on-acceleration prior in terms of solution accuracy.

  • full steam ahead exactly sparse gaussian process regression for batch continuous Time Trajectory estimation on se 3
    Intelligent Robots and Systems, 2015
    Co-Authors: Sean Anderson, Timothy D Barfoot
    Abstract:

    This paper shows how to carry out batch continuous-Time Trajectory estimation for bodies translating and rotating in three-dimensional (3D) space, using a very efficient form of Gaussian-process (GP) regression. The method is fast, singularity-free, uses a physically motivated prior (the mean is constant body-centric velocity), and permits Trajectory queries at arbitrary Times through GP interpolation. Landmark estimation can be folded in to allow for simultaneous Trajectory estimation and mapping (STEAM), a variant of SLAM.

  • batch nonlinear continuous Time Trajectory estimation as exactly sparse gaussian process regression
    arXiv: Robotics, 2014
    Co-Authors: Sean Anderson, Timothy D Barfoot, Chi Hay Tong, Simo Sarkka
    Abstract:

    In this paper, we revisit batch state estimation through the lens of Gaussian process (GP) regression. We consider continuous-discrete estimation problems wherein a Trajectory is viewed as a one-dimensional GP, with Time as the independent variable. Our continuous-Time prior can be defined by any nonlinear, Time-varying stochastic differential equation driven by white noise; this allows the possibility of smoothing our Trajectory estimates using a variety of vehicle dynamics models (e.g., `constant-velocity'). We show that this class of prior results in an inverse kernel matrix (i.e., covariance matrix between all pairs of measurement Times) that is exactly sparse (block-tridiagonal) and that this can be exploited to carry out GP regression (and interpolation) very efficiently. When the prior is based on a linear, Time-varying stochastic differential equation and the measurement model is also linear, this GP approach is equivalent to classical, discrete-Time smoothing (at the measurement Times); when a nonlinearity is present, we iterate over the whole Trajectory to maximize accuracy. We test the approach experimentally on a simultaneous Trajectory estimation and mapping problem using a mobile robot dataset.

  • batch continuous Time Trajectory estimation as exactly sparse gaussian process regression
    Robotics: Science and Systems, 2014
    Co-Authors: Timothy D Barfoot, Chi Hay Tong, Simo Sarkka
    Abstract:

    In this paper, we revisit batch state estimation through the lens of Gaussian process (GP) regression. We consider continuous-discrete estimation problems wherein a Trajectory is viewed as a one-dimensional GP, with Time as the independent variable. Our continuous-Time prior can be defined by any linear, Time-varying stochastic differential equation driven by white noise; this allows the possibility of smoothing our Trajectory estimates using a variety of vehicle dynamics models (e.g., ‘constant-velocity’). We show that this class of prior results in an inverse kernel matrix (i.e., covariance matrix between all pairs of measurement Times) that is exactly sparse (block-tridiagonal) and that this can be exploited to carry out GP regression (and interpolation) very efficiently. Though the prior is continuous, we consider measurements to occur at discrete Times. When the measurement model is also linear, this GP approach is equivalent to classical, discrete-Time smoothing (at the measurement Times). When the measurement model is nonlinear, we iterate over the whole Trajectory (as is common in vision and robotics) to maximize accuracy. We test the approach experimentally on a simultaneous Trajectory estimation and mapping problem using a mobile robot dataset.

Yancheng Liu - One of the best experts on this subject based on the ideXlab platform.

  • nonlinear disturbance observer based backstepping finite Time sliding mode tracking control of underwater vehicles with system uncertainties and external disturbances
    Nonlinear Dynamics, 2017
    Co-Authors: Siyuan Liu, Yancheng Liu, Ning Wang
    Abstract:

    In this paper, a nonlinear disturbance observer-based backstepping finite-Time sliding mode control scheme for Trajectory tracking of underwater vehicles subject to unknown system uncertainties and Time-varying external disturbances is proposed. To reduce the influence of the uncertainties and external disturbances, a nonlinear disturbance observer is developed without any acceleration measurements to identify the lumped disturbance term. Additionally, the finite-Time Trajectory tracking controller is designed by combining second-order sliding mode control and backstepping design technique with the nonlinear disturbance observer. The finite-Time convergence of motion tracking errors and the stability of the overall closed-loop control system are guaranteed by the Lyapunov approach. Besides, comprehensive simulation studies on Trajectory tracking control of underwater vehicles are provided to demonstrate the effectiveness and performance of the proposed control scheme.

  • adaptive robust finite Time Trajectory tracking control of fully actuated marine surface vehicles
    IEEE Transactions on Control Systems and Technology, 2016
    Co-Authors: Ning Wang, Chunjiang Qian, Jingchao Sun, Yancheng Liu
    Abstract:

    In this brief, an adaptive robust finite-Time tracking control (ARFTTC) scheme for Trajectory tracking of a fully actuated marine surface vehicle with unknown disturbances is proposed. A new finite-Time disturbance observer is incorporated into the proposed finite-Time tracking control (FTTC) structure that facilitates faster convergence and better robustness to disturbances. Hence, in the presence of unknown disturbances, the ARFTTC can cause tracking error to converge to zero in a finite Time. Simulation studies and comprehensive comparisons with conventional backstepping technique demonstrate remarkable performance and superiority of the ARFTTC in terms of both tracking accuracy and robustness.

Kathryn F Graham - One of the best experts on this subject based on the ideXlab platform.

  • minimum Time Trajectory optimization of low thrust earth orbit transfers with eclipsing
    Journal of Spacecraft and Rockets, 2016
    Co-Authors: Kathryn F Graham
    Abstract:

    The problem of determining high-accuracy minimum-Time Earth-orbit transfers using low-thrust propulsion with eclipsing is considered. The orbit transfer problem is posed as a multiple-phase optimal control problem where the spacecraft can thrust only during phases where it has line of sight to the sun. Event constraints, based on the geometry of a penumbra shadow region, are enforced between the phases and determine the amount of Time spent in an eclipse. An initial guess generation method is developed that constructs a useful guess by solving a series of single-phase optimal control problems and analyzing the resulting Trajectory to approximate where the spacecraft enters and exits the Earth’s shadow. The single-phase and multiple-phase optimal control problems are solved using an hp adaptive Legendre–Gauss–Radau orthogonal collocation method. To demonstrate the effectiveness of the approach developed in this research, optimal transfer trajectories are computed for two Earth-orbit transfers found in the ...

  • minimum Time Trajectory optimization of multiple revolution low thrust earth orbit transfers
    Journal of Spacecraft and Rockets, 2015
    Co-Authors: Kathryn F Graham
    Abstract:

    The problem of determining high-accuracy minimum-Time Earth-orbit transfers using low-thrust propulsion is considered. The optimal orbital transfer problem is posed as a constrained nonlinear optimal control problem and is solved using a variable-order Legendre–Gauss–Radau quadrature orthogonal collocation method. Initial guesses for the optimal control problem are obtained by solving a sequence of modified optimal control problems where the final true longitude is constrained and the mean square difference between the specified terminal boundary conditions and the computed terminal conditions is minimized. It is found that solutions to the minimum-Time low-thrust optimal control problem are only locally optimal, in that the solution has essentially the same number of orbital revolutions as that of the initial guess. A search method is then devised that enables computation of solutions with an even lower cost where the final true longitude is constrained to be different from that obtained in the original ...

  • minimum Time Trajectory optimization of multiple revolution low thrust earth orbit transfers
    Journal of Spacecraft and Rockets, 2015
    Co-Authors: Kathryn F Graham, Anil V Rao
    Abstract:

    The problem of determining high-accuracy minimum-Time Earth-orbit transfers using low-thrust propulsion is considered. The optimal orbital transfer problem is posed as a constrained nonlinear optim...

Kyoung Kwan Ahn - One of the best experts on this subject based on the ideXlab platform.

  • disturbance observer based finite Time Trajectory tracking control for a 3 dof hydraulic manipulator including actuator dynamics
    IEEE Access, 2018
    Co-Authors: To Xuan Dinh, Tran Duc Thien, Truong Hoai Vu Anh, Kyoung Kwan Ahn
    Abstract:

    This paper gives the kinematic description and mathematical dynamic model of a three-degrees-of-freedom manipulator, including hydraulic actuator dynamics, and then proposes a disturbance observer-based robust control scheme for the position and torque tracking control subjected to the external disturbances and parameter uncertainties. First of all, the kinematic of the manipulator system is built according to the Denavit–Hartenberg notation. The joint space, actuator space, and the mathematical model of the manipulator, including hydraulic actuator dynamics, are then presented. Next, a robust control technique is designed for the fast and finite-Time tracking capability of the torque signals along the desired torque commands, and a fast nonsingular terminal sliding mode control algorithm is developed to guarantee the fast convergence of the joint positions to their desired values. Moreover, two disturbance observer schemes are proposed to estimate and compensate the external disturbances and modeling errors in the manipulator system and hydraulic actuator system. Stability analysis of the cascade hydraulic manipulator system is analyzed and proved using the backstepping technique and Lyapunov theory. Finally, numerical simulations are obtained to validate the effectiveness of the designed control algorithm.