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Predictive control of a class of bilinear systems based on global off-line models

机译:基于全局离线模型的一类双线性系统的预测控制

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摘要

A new multi-step adaptive predictive control algorithm for a class of bilinear systems is presented. The structure of the bilinear system is converted into a simple linear model by using nonlinear support vector machine (SVM) dynamic approximation with analytical control law derived. The method does not need on-line parameters estimation because the system's internal model has been transformed into an off-line global model. Compared with other traditional methods, this control law reduces on-line parameter estimating burden. In addition, its overall linear behavior treating method allows an analytical control law available and avoids on-line nonlinear optimization. Simulation results are presented in the article to illustrate the efficiency of the method.
机译:提出了一种用于一类双线性系统的新的多步自适应预测控制算法。利用非线性支持向量机(SVM)动态逼近并导出分析控制律,将双线性系统的结构转换为简单的线性模型。该方法不需要在线参数估计,因为系统的内部模型已转换为离线全局模型。与其他传统方法相比,该控制律减少了在线参数估计负担。另外,其整体线性行为处理方法可提供可用的分析控制律,并避免了在线非线性优化。本文提供了仿真结果,以说明该方法的有效性。

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