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首页> 外文期刊>Quality Control, Transactions >Neural-Based Command Filtered Backstepping Control for Trajectory Tracking of Underactuated Autonomous Surface Vehicles
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Neural-Based Command Filtered Backstepping Control for Trajectory Tracking of Underactuated Autonomous Surface Vehicles

机译：基于神经基的命令过滤了渗透跟踪的反向驱动的轨迹跟踪

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摘要

This paper is concerned with the problem of trajectory tracking control of underactuated autonomous surface vehicles subject to parameter uncertainties and nonlinear external disturbances. A robust control scheme is presented by employing backstepping method, neural network and sliding mode control. In addition, the overall signals are guaranteed the uniformly ultimate boundness by the Lyapunov stability theory. These advantages are highlighted as follows: (i) The derivations of virtual variables are obtained by a second-order filter. A compensation loop is proposed to reduce the filtered errors between the filtered variables and virtual variables. (ii) The neural network is combined with low-frequency learning techniques to estimate and approximate unknown functions of system.(iii) An anti-windup design is employed to restrict the amplitude of control inputs. Finally, simulation results show the strong robustness and tracking effectiveness of the designed control scheme under the nonlinear external disturbances.
机译：本文涉及受限于参数不确定因素和非线性外部干扰的欠扰自主表面车辆的轨迹跟踪控制问题。通过采用反静电方法，神经网络和滑动模式控制来呈现鲁棒控制方案。此外，整体信号是Lyapunov稳定性理论的统一最终的界限。这些优点如下突出显示：（i）通过二阶滤波器获得虚拟变量的推导。提出补偿循环以减少滤波的变量和虚拟变量之间的滤波错误。 （ii）神经网络与低频学习技术相结合以估计和近似系统的未知功能。（iii）采用防卷化设计来限制控制输入的幅度。最后，仿真结果表明，非线性外部干扰下设计控制方案的强大稳健性和跟踪有效性。

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