Koopman Operator-based Model Predictive Control with Recursive Online Update
Koopman Operator-based Model Predictive Control with Recursive Online Update
复制标题
基于库普曼算子的递归在线更新模型预测控制
DOI：
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发表时间：
2021
期刊：
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通讯作者：
H. Werner
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文献类型：
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作者：
Horacio M. Calderón;Erik Schulz;T. Oehlschlägel;H. Werner
库普曼算子框架允许将非线性系统嵌入到线性系统中。这使得用线性方法分析、估计和控制非线性动力学成为可能。基于Koopman算子的控制器通常是模型预测控制（MPC）方案。MPC的性能取决于其模型的预测精度。因此，如果预测不够准确，在线更新模型是有意义的。在这项工作中，我们使用带遗忘因子的递归最小二乘（RLS）算法来解决这个问题。此外，我们在一个实证案例研究中表明，将KO与在线更新和最近提出的准线性参数变模型预测控制（qLMPC）算法相结合，可以得到一种有效的控制方案。
The Koopman operator framework allows to embed a nonlinear system into a linear one. This enables the analysis, estimation, and control of nonlinear dynamics with linear methods. Controllers based on the Koopman operator (KO) are often model predictive control (MPC) schemes. The performance of an MPC depends on the prediction accuracy of its model. Hence, it is meaningful to update the model online if the predictions are not sufficiently accurate. In this work, we approach this problem by using a recursive least squares (RLS) algorithm with forgetting factor. Furthermore, we show in an empirical case study that combining the KO with an online update and the recently proposed quasi-linear parameter-varying model predictive control (qLMPC) algorithm results in an efficient control scheme.