输入饱和下AUV自适应神经网络预设性能控制

Adaptive Neural Network-Based Prescribed Performance Control of AUVs with Input Saturation

针对自主水下航行器(AUV)系统不确定性及输入饱和问题,提出了一种改进的自适应神经网络预设性能控制策略,完成对期望轨迹的跟踪。首先,引入非线性变换,使位置误差始终处在预设时变范围内,提高了控制精度,并基于反步法和Lyapunov函数设计系统虚拟控制律;然后,利用神经网络技术处理系统模型未知参数,并重构系统真实控制律,使传统反步控制策略得以简化,有效降低了计算复杂度,并在Lyapunov稳定性理论的基础上,证明了AUV系统的误差信号均有界;最后,与传统动态面控制方法进行对比,仿真结果表明所提出的控制策略控制性能更好,在考虑输入饱和情况下可有效克服不确定性对系统性能的影响,实现对目标轨迹的有效跟踪。

In view of system uncertainty and input saturation of autonomous undersea vehicles(AUVs),an improved adaptive neural network-based prescribed performance control strategy was proposed to track the desired trajectory.Firstly,the nonlinear transformation was introduced to ensure that the position error remained within the preset time-varying range,improving control accuracy.Based on backstepping and Lyapunov functions,a virtual control law for the system was designed.Then,the neural network technology was used to process the unknown parameters of the system model,and the real control law of the system was reconstructed,which simplified the traditional backstepping control strategy and effectively reduced the computational complexity.Then,based on the Lyapunov stability theory,all the error signals of the AUV system were confirmed to be bounded.Finally,compared with traditional dynamic surface control methods,the simulation results show that the proposed control strategy has better control performance and can effectively overcome the impact of uncertainty on system performance by considering input saturation,effectively tracking target trajectories.