Anti-saturation Control Design for Remote Operated Vehicle Considering Model Error
-
摘要: 为研究新型有缆开架式遥控水下航行器(ROV)的轨迹跟踪问题, 在径向基神经网络滑膜控制律基础上, 考虑推力约束条件, 提出一种变增益抗饱和辅助系统, 并通过李雅普诺夫稳定性相关定理证明了控制系统的稳定性。该系统充分考虑了ROV的实际作业工况, 利用MARLAB Simulik搭建仿真平台, 在轨迹跟踪仿真验证中引入建模误差、海流干扰、脐带缆作用力等因素影响。仿真结果表明, 加入抗饱和系统后, ROV进行轨迹跟踪时推进系统推力饱和持续时间降低27%, 各自由度的累计跟踪误差降低, 垂荡和横摇方向的跟踪误差大幅减少, 验证了新控制律的可靠性。Abstract: To investigate the trajectory-tracking problems of a new open-frame remote operated vehicle(ROV), a new variable gain anti-saturation auxiliary system is proposed, which is based on the radial basis function neural network sliding mode control law and considers thrust constraints. The stability of the control law was proved using the Lyapunov stability theory. A simulation was built using MATLAB Simulink, and modeling errors, ocean current disturbances, and cable action force are considered during the trajectory-tracking study, which is similar to the actual working environment of a ROV. The results show that after incorporating the anti-saturation system, the thrust saturation duration of the propulsion system decreased by 27% during the ROV track tracking, the cumulative tracking error of each degree of freedom decreased, and the tracking error of the roll and heave degrees decreased significantly, demonstrating the reliability of the new control law.
-
[1] 王啸. 基于自适应鲁棒算法的开架ROV悬停姿态控制研究[D]. 青岛: 中国海洋大学, 2014. [2] Fernandes D D, Donha D C. Optimal Control System for a Semi-Autonomous Underwater Vehicle[J]. IFAC Proceedings Volumes, 2009, 42(18): 255-260. [3] Fernandes D D, Sorensen A J, Pettersen K Y, et al. Output Feedback Motion Control System for Observation Class ROVs Based on a High-gain State Observer: Theoretical and Experimental Results[J]. Control Engineering Practice, 2015, 39: 90-102. [4] 朱琦. 作业型水下机器人姿态控制方法研究[D]. 杭州: 浙江大学, 2018. [5] Chu Z, Xiang X, Zhu D, et al. Adaptive Fuzzy Sliding Mode Diving Control for Autonomous Underwater Vehicle with Input Constraint[J]. International Journal of Fuzzy Systems, 2018, 20(5): 1460-1469. [6] 刘金琨. RBF神经网络自适应控制Matlab 仿真[M]. 北京: 清华大学出版社, 2014: 57-60. [7] 夏俊. 基于RBF神经网络的无人水面舰艇自适应控制[J]. 机械制造与自动化, 2019, 48(3): 185-188. [8] Fossen T I. Handbook of Marine Craft Hydrodynamics and Motion Control[M]. USA: John Wiley & Sons, Inc., 2011. [9] Huo X, Ge T, Wang X, et al. Horizontal Path-following Control for Deep-sea Work-class ROVs Based on a Fuzzy Logic System[J]. Ships and Offshore Structures, 2018, 13(6): 637-648. [10] Chen M, Ge S S, Ren B, et al. Adaptive Tracking Control of Uncertain MIMO Nonlinear Systems with Input Constraints[J]. Automatica, 2011, 47(3): 452-465. [11] 徐诗婧. 开架式ROV水动力特性与运动仿真研究[D]. 哈尔滨: 哈尔滨工程大学, 2018. [12] 薛乃耀, 王冬姣, 叶家玮, 等. 开架式水下机器人操纵性水动力系数计算[J]. 广东造船, 2020, 39(1): 25-28.Xue Nai-yao, Wang Dong-jiao, Ye Jia-wei, et al. Maneuverability Coefficients Calculation of an Open-frame Underwater Remote Operated Vehicle[J]. Guangdong Ship-building, 2020, 39(1): 25-28. [13] Salgado-Jimnez T, García-Valdovinos L G, Delgado- Ramírez G. Control of ROVs Using a Model-free 2nd- Order Sliding Mode Approach[EB/OL]. Sliding Mode Control. (2011-04-11)[2019-09-18]. https://www.intechopen. com/books/sliding-mode-control/control-of-rovs-using-a-model-free-2nd-order-sliding-mode-approach. -
点击查看大图
计量
- 文章访问数: 125
- HTML全文浏览量: 1
- PDF下载量: 101
- 被引次数: 0
下载:
