Hierarchical T-S fuzzy-neural control of anti-lock braking system and active suspension in a vehicle
Hierarchical T-S fuzzy-neural control of anti-lock braking system and active suspension in a vehicle
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DOI：
10.1016/j.automatica.2012.05.033
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发表时间：
2012-08
期刊：
影响因子：
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通讯作者：
Wei-Yen Wang;Ming-Chang Chen;S. Su
中科院分区：
文献类型：
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作者：
Wei-Yen Wang;Ming-Chang Chen;S. Su
提出了一种基于T-S模糊神经网络模型的主动悬架防抱死制动系统的辨识与鲁棒自适应控制方法。传统ABS控制系统的目标是快速消除实际滑移率和设定参考值之间的跟踪误差，以便在尽可能短的时间内使车辆停止。然而，如果同一控制系统还同时考虑主动悬架系统的状态，则制动时间和停车距离甚至可以进一步减少。提出的分层T-S模糊神经网络的结构学习能力，以减少计算时间，和模糊规则的数量。因此，该控制器被应用于实现集成控制的防抱死制动系统（ABS）与主动悬架系统。我们的仿真结果，在本文的最后，表明所提出的控制器是非常有效的ABS和主动悬架系统的综合控制。
This paper proposes a novel method for identification and robust adaptive control of an anti-lock braking system with an active suspension system by using the hierarchical Takagi–Sugeno (T–S) fuzzy-neural model. The goal of a conventional ABS control system is to rapidly eliminate tracking error between the actual slip ratio and a set reference value in order to bring the vehicle to a stop in the shortest time possible. However, braking time and stopping distance can be reduced even further if the same control system also simultaneously considers the state of the active suspension system. The structure learning capability of the proposed hierarchical T–S fuzzy-neural network is exploited to reduce computational time, and the number of fuzzy rules. Thus, this proposed controller is applied to achieve integrated control over the anti-lock braking system (ABS) with the active suspension system. Our simulation results, presented at the end of this paper, show that the proposed controller is extremely effective in integrated control over the ABS and the active suspension system.