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Nonlinear Predictive Control Based on Least Squares Support Vector Machines Hammerstein Models

机译:基于最小二乘支持向量机Hammerstein模型的非线性预测控制

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This paper shortly describes nonlinear Model Predictive Control (MPC) algorithms for Least Squares Support Vector Machines (LS-SVM) Hammerstein models. The model consists of a nonlinear steady-state part in series with a linear dynamic part. A linear approximation of the model for the current operating point or a linear approximation of the predicted output trajectory along an input trajectory is used for prediction. As a result, all algorithms require solving on-line a quadratic programming problem or a series of such problems, unreliable and computationally demanding nonlinear optimisation is not necessary.
机译:本文简要介绍了用于最小二乘支持向量机(LS-SVM)Hammerstein模型的非线性模型预测控制(MPC)算法。该模型由与线性动态部分串联的非线性稳态部分组成。当前工作点的模型的线性近似或沿着输入轨迹的预测输出轨迹的线性近似用于预测。结果,所有算法都需要在线解决二次编程问题或一系列此类问题,因此不需要不可靠且计算量大的非线性优化。

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