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Robust Predictive Control Based on Neighboring Extremals

机译:基于邻居极值的强大预测控制

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The performance of predictive controller is typically poor when the true plant evolution deviates significantly from that predicted by the model. A robust approach that considers model uncertainty explicitly is then needed. However, it is often difficult to find a single input profile that works for the range of uncertainty considered. Thus, multiple input profiles, i.e. one for each realization of the uncertainty, need to be determined, which is computationally extremely expensive. This paper proposes an alternative approach, based on neighboring extremals, where the multiple input profiles are computed using a simple feedback law, thereby reducing considerably the computational burden. The idea is illustrated via the simulation of an inverted pendulum on a cart.
机译:当真正的植物演变显着地从模型预测的情况下偏离时,预测控制器的性能通常很差。然后需要一种明确地考虑模型不确定性的强大方法。但是,通常很难找到用于考虑的不确定性范围的单个输入配置文件。因此,需要确定多个输入配置文件,即每个实现不确定性的一个不确定性,这是计算地非常昂贵的。本文提出了一种基于相邻极值的替代方法,其中使用简单的反馈法计算多输入轮廓,从而减少了计算负担。通过在推车上模拟倒挂摆动来示出该想法。

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