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Intelligent vehicle lateral controller design based on genetic algorithmand T-S fuzzy-neural network

Journal of Systems Engineering and Electronics 2005年第2期16卷 382-387页

作者： RuanJiuhong FuMengyin LiYibin SchoolofInformationandScienceTechnology BeijingInstituteofTechnologyBeijing100081P.R.China SchoolofControlScienceandEngineering ShandongUniversityJinan250031P.R.China

Non-linearity and parameter time-variety are inherent properties of lateral motions of a vehicle. How to effectively control intelligent vehicle (IV) lateral motions is a challenging task. Controller design can be regarded as a process of searching optimal structure from controller structure space and searching optimal parameters from parameter space. Based on this view, an intelligent vehicle lateral motions controller was designed. The controller structure was constructed by T-S fuzzy-neural network (FNN). Its parameters were searched and selected with genetic algorithm (GA). The simulation results indicate that the controller designed has strong robustness, high precision and good ride quality, and it can effectively resolve IV lateral motion non-linearity and time-variant parameters problem.

关键词： intelligent vehicle genetic algorithm fuzzy-neural network lateral control robustness.

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Intelligent Decision-Making for Tactics Mission and Train Base on Tandem fuzzy-neural network

Intelligent Decision-Making for Tactics Mission and Train Ba...

引用

2011 International Conference on Photonics, 3D-Imaging, and Visualization

作者： Ge XianJun Song WeiWei Tang Jing Department of Scientific Research of NAAU The 92323th Unit of PLA Department of Automatic Control Engineering of NAAU

ISBN: (纸本)9780819488473

For Navy aviation Force, Mission-making and Train is very important. Mission-making is a process of choosing among alternative courses of action for solving complicated problems where multi-criteria objectives are involved. We have witnessed a growing recognition of computing technologies that underlie the conception, design and utilization of intelligent systems in the past few years. Several works have been done where we have applied intelligent techniques and heuristics to obtain optimal decisions from imprecise information. In this paper, we present a tandem fuzzy-neural network approach and Reduced Memory Levenberg-Marquardt training approach combining unsupervised and supervised learning techniques to develop the Tactical Mission and Train Decision System（TMTDS）. Experiment results clearly demonstrate the efficiency of the proposed technique.

关键词： fuzzy-neural network Training Mission Support System

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