基于反馈增益的AUV稳定神经网络反步变深控制

为解决自主水下航行器的变深控制问题,提出一种基于反馈增益的反步控制方法.首先,通过设计控制器参数消除部分非线性项,在保证系统稳定性的同时设计神经网络控制器来补偿纵倾运动中的模型不确定性；然后,通过自适应鲁棒控制器对神经网络的逼近误差予以消除,以加快神经网络的收敛学习速度,神经网络权值和逼近误差估计的学习律可由李雅普诺夫稳定性理论推导得出,保证了闭环系统的一致最终有界性；最后,通过仿真实验验证了所提出方法的有效性.

Stable Neural Network Backstepping for Diving Control of AUV based

To solve the diving control problem of an autonomous underwater vehicle(AUV), the backstepping method
based on feedback gain is proposed for the controller design. Firstly, part of coupled nonlinear terms can be eliminated through choosing the controller’s parameter with guaranteed stability. Model uncertainties including nonlinear hydrodynamic damping terms and external disturbance in pitch motion are compensated by neural network(NN) controller online, while the approximation error of the neural network can be eliminated by the adaptive robust compensator to increase the convergence learning rate of neural network. Both neural network strength and adaptive laws are obtained through the Lyapunov stability theorem, and uniformly ultimately bounded(UUB) can be guaranteed for the closed-loop system. Finally, simulation results show the effectiveness of the proposed control scheme.