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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Backstepping Fuzzy-Neural-Network Control Design for Hybrid Maglev Transportation System
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Backstepping Fuzzy-Neural-Network Control Design for Hybrid Maglev Transportation System

机译：混合磁悬浮运输系统的Backstepping模糊神经网络控制设计

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摘要

This paper focuses on the design of a backstepping fuzzy-neural-network control (BFNNC) for the online levitated balancing and propulsive positioning of a hybrid magnetic levitation (maglev) transportation system. The dynamic model of the hybrid maglev transportation system including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics is first constructed. The ultimate goal is to design an online fuzzy neural network (FNN) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BFNNC scheme, an FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control system in previous research.
机译：本文重点研究一种用于混合磁悬浮（磁悬浮）运输系统的在线悬浮平衡和推进定位的反步模糊神经网络控制（BFNNC）。首先建立了混合磁悬浮运输系统的动力学模型，该模型包括悬浮的混合电磁体以减少线性运动过程中的悬挂功率损耗和摩擦力，并基于机械几何和运动动力学的概念构建了一种推进式线性感应电动机。最终目标是设计一种在线模糊神经网络（FNN）控制方法，以解决复杂的控制转换问题和Backstepping控制（BSC）设计中的颤动控制问题，并直接确保受控系统的稳定性而无需尽管存在不确定性，但仍然需要严格的约束条件，详细的系统信息和辅助补偿控制器。在提出的BFNNC方案中，通过模仿BSC策略，将FNN控制用作主要控制角色，并从投影算法和Lyapunov稳定性定理的意义上推导了网络参数的自适应律，以确保网络收敛和稳定控制性能。实验结果验证了所提出的混合磁悬浮运输系统控制策略的有效性，并与以往的BSC策略和反推粒子群优化控制系统相比，表明了BFNNC方案的优越性。

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