期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2014
卷号：11
DOI：10.5772/58578
语种：English
出版社：SAGE Publications
摘要：Open-frame is one of the major types of structures of Remote Operated Vehicles (ROV) because it is easy to place sensors and operations equipment on-board. Firstly, this paper designed a petri-based recurrent neural network (PRFNN) to improve the robustness with response to nonlinear characteristics and strong disturbance of an open-frame underwater vehicle. A threshold has been set in the third layer to reduce the amount of calculations and regulate the training process. The whole network convergence is guaranteed with the selection of learning rate parameters. Secondly, a fault tolerance control (FTC) scheme is established with the optimal allocation of thrust. Infinity-norm optimization has been combined with 2-norm optimization to construct a bi-criteria primal-dual neural network FTC scheme. In the experiments and simulation, PRFNN outperformed fuzzy neural networks in motion control, while bi-criteria optimization outperformed 2-norm optimization in FTC, which demonstrates that the FTC controller can improve computational efficiency, reduce control errors, and implement fault tolerable thrust allocation.
