基于速度调节的无人帆船机器人自适应航向保持控制

Adaptive course-keeping control for unmaned sailboat robot with the speed regulating mechanism

中文关键词  无人帆船  速度调节  RBF神经网络  增益不确定  自适应控制

英文关键词  unmanned sailboat  speed regulating  radial basic function neural networks  gain uncertainty  adaptive control

中文摘要

      为了解决实际海洋环境下, 无人帆船机器人(USR)航向保持控制任务中存在的模型结构未知、参数不确定 和航行速度难以控制等问题, 本文提出一种具有速度调节性能的鲁棒自适应航向保持控制算法. 该算法采用径向 基(RBF)神经网络对系统结构不确定进行逼近, 由于引入鲁棒神经阻尼技术和动态面控制技术, 使得闭环控制系统 仅需要两个自适应参数对执行器的增益不确定部分进行在线补偿, 并且不需要对神经网络权重参数进行学习更新. 所提出的控制算法能够有效控制无人帆船以期望的航行速度达到设定航向. 利用Lyapunov稳定性理论证明了所提 出控制器能够保证闭环控制系统中相关误差变量满足半全局一致最终有界(SGUUB)收敛. 通过在模拟海洋环境干 扰下进行计算机仿真研究, 验证了所提出算法具有良好的速度调节性能和鲁棒性.

英文摘要

      This paper deals with the course-keeping control problem for the unmanned sailboat robot (USR), aiming to the unknown model structure, parameters uncertainty and speed control difficulty in the practical marine environment. For this purpose, a novel robust adaptive course keeping control algorithm with speed regulating is developed. In this algorithm, the radial basic function (RBF) neural network is employed to approximate the structure’s uncertainty. Due to the merits of the robust neural damping technique and the dynamic surface control, only two adaptive parameters are designed to compensate the actuators’ gain uncertainty. In addition, the information of the neural networks weights parameters is not needed. The USR could converge to the objective course with the desired navigational speed under the proposed controller. Sufficient effort has been made to guarantee that the corresponding error variables satisfies the semi-global uniform ultimate bounded (SGUUB) stability via the Lyapunov theory. Through the computer simulation under the presence of marine environment, the proposed approach could obtain the better performance in aspects of the speed regulating and robustness.