基于双体结构的无人测绘艇航向控制

Course Control of Unmanned Surveying and Mapping Vehicle Based on Catamaran Structure

无人艇在海洋测绘中的应用越来越广泛,而航向稳定性是实现其自主航行的重要基础。设计了一种双体结构的无人测绘艇,用于搭载测量设备并实现测绘功能。为了实现无人艇的航向控制,将RBF(radial basis function)神经网络与PID(proportion integration differentiation)算法相结合,利用RBF神经网络的自学习能力实现对PID控制器参数的整定。在仿真过程中,将RBF-PID自适应模型、单一PID模型以及作为对照组引入的BP-PID自适应模型分别仿真,并在同一时刻加入随机扰动,观察系统响应效果。结果表明,基于RBF-PID算法的无人艇航向控制器的超调量为零、稳态时间最短,同时能够及时有效地纠正随机扰动的影响,保障无人测绘艇的航向稳定性。

The unmanned surface vehicles are more and more widely applied in ocean surveying and mapping.The course stability is an important basis for its autonomous navigation.An unmanned surveying and mapping based on catamaran structure was designed for carrying measurement equipment and achieving mapping function.In order to realize course control of the unmanned vehicle,the radial basis function(RBF)neural network was combined with the PID(proportion integration differentiation)algorithm,and the self-learning ability of the RBF neural network was used to adjust the parameters of the PID controller autonomously.During simulation,the RBF-PID adaptive model,the single PID model and the BP-PID adaptive model were simulated separately,and random disturbances were added at a same time to observe the system response.The BP-PID adaptive model was introduced as the control group.The results show that the course controller based on RBF-PID algorithm has zero overshoot and the shortest steady-state time.At the same time,it can correct the influence of random disturbances timely and effectively ensure the heading stability of the unmanned surveying and mapping vehicle.