垂直面欠驱动自治水下机器人定深问题的自适应输出反馈控制

针对垂直面欠驱动自治水下机器人(AUV)定深控制问题,本文仅使用可测量的深度和纵摇角信息,基于反步法设计自适应输出反馈控制器.为此首先设计观测器,实现不可测纵摇角速度反馈;再利用径向基神经网络对不确定水动力系数和纵荡、垂荡及纵摇角速度耦合产生的非线性结构进行补偿;采用自适应策略对纵荡和垂荡速度形成的有界干扰进行抑制.本文采用AUV一阶非完整模型,不以线性化为目的,放宽了纵摇角只能在小范围内变化的限制.最后通过理论证明和仿真实验表明该方法能够实现AUV深度和姿态控制,对未建模非线性动态和有界扰动具有很强的自适应性和鲁棒性.

Depth-keeping control for autonomous underwater vehicle in vertical plane using adaptive output feedback controller

To keep the depth of an autonomous underwater vehicle (AUV) in a vertical plane, we design an adaptive output feedback controller based on the backstepping technique, using the information of measurable depth and the pitch angle. To achieve this objective, an observer is built to implement the feedback of the immeasurable velocity of the pitch angle. A radial basis neural network is adopted to compensate the nonlinear effects produced by the uncertain hydrodynamic coefficients as well as the coupling velocities of surge, dive, and pitch angle. An adaptive strategy is used to inhibit the disturbance from the velocity of surge and dive. A first-order incomplete model of AUV is employed without linearization; this relaxes the restriction on the variation range of the pitch angle. Theoretical analysis and simulation experiment results show that the proposed method implements the control of depth and altitude of the AUV with high adaptability and strong robustness for the unordered nonlinear dynamics and bounded disturbances.