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无舵翼水下机器人路径跟踪控制研究   总被引:1,自引:0,他引:1  
针对无舵翼水下机器人的各种不同任务要求下的路径跟踪控制进行研究。通过模拟人的运动行为,建立了虚拟避碰声纳模型。根据地形跟踪的方法提出基于虚拟声纳的路径跟踪控制方法,并通过考虑纵向速度对于其他各个自由度运动的影响设计了运动控制器。通过海上试验验证了所提出的路径跟踪控制方法对于无舵翼水下机器人是可以满足实际需要的。  相似文献   
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A parallel neural network-based controller (PNNC) is presented for the motion control of underwater vehicles in this paper. It consists of a real-time part, a self-learning part and a desired-state programmer, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. The developed controller is applied to the 4-degree of freedom control of the AUV “IUV- IV” and is successful on the simulation platform. The control performance is also compared with that of neural network controller with different structures such as normal adaptive neural network and different learning methods. Current effects and surge velocity control are also included to demonstrate the controller' s performance. It is shown that the PNNC has a great possibility to solve the problems in the control system design of underwater vehicles.  相似文献   
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GDROV是用于堤坝探测的水下机器人,设计上属于开架式机器人,其精确的数学模型很难获得.采用基于模糊逻辑的直接自适应控制方法,利用模糊基函数网络逼近理想控制输出,通过模糊逻辑动态调整控制器的参数自适应律,可有效解决水下机器人控制问题.建立GDROV的水动力模型,给出基于模糊逻辑的直接自适应控制算法,最后通过仿真试验和外场试验验证了该控制器对模型的不确定性具有较强的鲁棒性,且具有良好的跟踪性能.  相似文献   
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