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Representation of an Integrated Non-Linear Model-Based Predictive Vehicle Dynamics Control System by a Co-Active Neuro-Fuzzy Inference System

机译:由共振神经模糊推理系统表示集成的非线性模型的预测车辆动力学控制系统

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In the context of automated driving, the control of vehicle dynamics is one of the important issues. In addition to conventional control strategies, algorithms with predictive working principles are particularly relevant here. Using mathematical models, the future system behavior can be predicted and thus optimally set. The present paper deals with an integrated non-linear model-based predictive vehicle dynamics control, taking into account the roll and pitch behavior of a vehicle. Due to the optimization, such model-based predictive control algorithms usually result in high computation efforts. With respect to this issue, a non-linear model-based predictive control algorithm regarding an integrated vehicle dynamics control is represented by a co-active neuro-fuzzy inference system. The validation of the two vehicle dynamics control algorithms is done with respect to the control quality and the computation effort.
机译:在自动驾驶的背景下,车辆动态的控制是重要问题之一。除了传统的控制策略之外,这里具有预测工作原理的算法尤其相关。使用数学模型,可以预测未来的系统行为,从而最佳地设置。本文涉及基于非线性模型的预测车辆动态控制,考虑到车辆的卷和俯仰行为。由于优化,基于模型的预测控制算法通常会导致高计算工作。关于该问题,关于集成车辆动态控制的基于非线性模型的预测控制算法由共同主动神经模糊推理系统表示。对两个车辆动态控制算法的验证是针对控制质量和计算工作完成的。

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