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arXiv 2310.19367eess.SYcs.SY
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基于优化的伪线性化模型预测控制器设计:直接数据驱动方法

Optimized Pseudo-Linearization-Based Model Predictive Controller Design: Direct Data-Driven Approach

Mikiya Sekine, Satoshi Tsuruhara, Kazuhisa Ito

AI总结:

针对FRIT中参考模型选择困难导致性能下降的问题,提出一种基于伪线性化和模型预测控制的非线性系统数据驱动与模型结合的控制器设计方法,并通过仿真与人工肌肉实验验证了其有效性。

摘要:

为了减少基于模型的控制器设计中对象建模的典型耗时流程,fictitious reference iterative tuning (FRIT) 被提出并在许多应用中被证明是有效的。然而,在没有对象信息的情况下,通常很难恰当地选择参考模型,这会显著影响控制性能,有时会导致严重的性能下降。为了解决这个问题,我们提出了一种使用 FRIT 的伪线性化 (PL) 方法,并为非线性系统设计了一种结合数据驱动和基于模型控制的新控制器。该设计使用模型预测控制来考虑输入约束。所提方法的有效性通过非线性类别的数值模拟和涉及具有迟滞特性的人工肌肉的实验,根据几个实际参考进行了评估。

英文摘要:

To reduce the typical time-consuming routines of plant modeling for model-based controller designs, the fictitious reference iterative tuning (FRIT) has been proposed and has proven to be effective in many applications. However, it is generally difficult to select a reference model properly without information on the plant, which significantly affects the control performance and sometimes leads to considerable performance degradation. To address this problem, we propose a pseudo-linearization (PL) method using FRIT and design a new controller for nonlinear systems that combines data-driven and model-based control. This design considers the input constraints using model predictive control. The effectiveness of the proposed method was evaluated according to several practical references using numerical simulations for nonlinear classes and experiments involving artificial muscles with hysteresis characteristics.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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