自主式水下航行器三维路径跟踪的神经网络H∞鲁棒自适应控制方法

本文研究了存在模型不确定以及外界未知扰动情况下的自主式水下航行器(AUV)的三维路径跟踪控制问题. 针对此问题, 首先利用时标分离原理及正交投影Serret-Frenet坐标系建立了描述AUV质心运动及姿态运动的的仿射非线性数学模型. 其次, 在控制器设计中运用神经网络H∞鲁棒自适应算法克服了模型的不确定性及扰动, 同时在控制器设计中利用了主导输入的思想, 降低了闭环系统的复杂度, 减少了实时计算工作量, 便于工程应用. 基于Lyapunov理论的分析保证了系统的稳定性. 仿真结果表明, 路径跟踪控制律可以保证AUV沿期望路径运动, 并且具有良好的动态性能.

Neural network H-infinity robust adaptive control for autonomous underwater vehicle in 3-dimensional path following

The 3-dimensional path following control of autonomous underwater vehicle (AUV) with uncertain model and unknown disturbance is investigated. First, the time singular perturbation method and the orthogonal projection reference frame are used to model the position motion, and the attitude dynamic model of the AUV is described by an affine nonlinear system. Next, a neural network robust adaptive control algorithm is employed in the controller design to overcome the uncertainties of the model and the influence of external disturbances. Meanwhile, the dominant input idea is adopted to reduce the complexity of the closed-loop. The stability performance of the system is proved by using Lyaponov stability theory. Simulation indicates that the path following controller keeps the motion of AUV along the prescribed path with desirable performances.