This work is concerned with the predictive control of nonlinear systems using the Carleman bilinearization technique. Given a nonlinear system model with sufficient regularity, a bilinear approximation can be obtained by means of Carleman's technique. After discretizing the continuous time model, several algorithms exist to tackle the bilinear model predictive control (BMPC) problem. More specifically, the present work exploits the combination of Carleman's approximation with a fixed search directions algorithm previously proposed within the context of BMPC. Simulations are carried out in order to compare predictive controllers designed using bilinear and linear plant models, but acting on the original nonlinear system. Moreover, the fixed search directions algorithm is compared with the use of a standard interior point optimizer. As a result, the proposed method is shown to provide improvements in terms of either stability, constraint satisfaction or reduced computational effort.

中文翻译：

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采用卡尔曼双线性化和固定搜索方向的非线性预测控制

这项工作涉及使用卡尔曼双线性化技术对非线性系统进行预测控制。给定具有足够规律性的非线性系统模型，可以通过卡尔曼技术获得双线性近似。在对连续时间模型进行离散化之后，存在多种算法来解决双线性模型预测控制 (BMPC) 问题。更具体地说，目前的工作利用了 Carleman 近似与先前在 BMPC 上下文中提出的固定搜索方向算法的组合。进行仿真是为了比较使用双线性和线性对象模型设计但作用于原始非线性系统的预测控制器。此外，固定搜索方向算法与标准内点优化器的使用进行了比较。