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首页> 外文期刊>IFAC PapersOnLine >Model Predictive Control meets robust Kalman filtering * * This work has been partially supported by the FIRB project “Learning meets time” (RBFR12M3AC) funded by MIUR.
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Model Predictive Control meets robust Kalman filtering * * This work has been partially supported by the FIRB project “Learning meets time” (RBFR12M3AC) funded by MIUR.

机译：模型预测控制满足鲁棒的卡尔曼滤波 * * FIRB项目“学习遇见时间”（RBFR12M3AC ）由MIUR资助。

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摘要

Model Predictive Control (MPC) is the principal control technique used in industrial applications. Although it offers distinguishable qualities that make it ideal for industrial applications, it can be questioned its robustness regarding model uncertainties and external noises. In this paper we propose a robust MPC controller that merges the simplicity in the design of MPC with added robustness. In particular, our control system stems from the idea of adding robustness in the prediction phase of the algorithm through a specific robust Kalman filter recently introduced. Notably, the overall result is an algorithm very similar to classic MPC but that also provides the user with the possibility to tune the robustness of the control. To test the ability of the controller to deal with errors in modeling, we consider a servomechanism system characterized by nonlinear dynamics.
机译：模型预测控制（MPC）是工业应用中使用的主要控制技术。尽管它具有出色的质量，使其非常适合工业应用，但它在模型不确定性和外部噪声方面的坚固性值得质疑。在本文中，我们提出了一种鲁棒的MPC控制器，该控制器融合了MPC设计的简单性和增强的鲁棒性。特别是，我们的控制系统源自通过最近引入的特定鲁棒卡尔曼滤波器在算法的预测阶段增加鲁棒性的想法。值得注意的是，总体结果是与经典MPC非常相似的算法，但它也为用户提供了调整控件鲁棒性的可能性。为了测试控制器处理建模错误的能力，我们考虑了具有非线性动力学特征的伺服机构系统。

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