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首页> 外文会议>2011 First Workshop on Predictive Control of Electrical Drives and Power Electronics >The polynomial approximation of the explicit solution of model-based predictive controller for drive applications
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The polynomial approximation of the explicit solution of model-based predictive controller for drive applications

机译：基于模型的驱动器预测控制器显式解的多项式逼近

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The complexity of implementing the Explicit solution of Model-based Predictive Control (Exp-MPC) in drive applications results in a higher number of generated regions. This increases the required memory space to store these regions and the coefficients of the associated optimal control laws, even when the solution is implemented using an efficient algorithm like Binary Search Tree (BST). The proposed method aims to replace the optimal linear/affine control laws, defined over several regions of the feasible state space, with one polynomial. The polynomial will be multivariate, with higher order, and defined over a combination of feasible regions. Using the polynomial approximation, the necessary memory space, to store region coordinates and the coefficients of optimal control laws, is reduced significantly. Although the accuracy of the optimal controller is reduced with use of polynomial approximation, it makes the implementation more realistic; provided the stability and the feasibility of the problem is still guaranteed.
机译：在驱动器应用中实施基于模型的预测控制（Exp-MPC）显式解决方案的复杂性导致生成区域的数量增加。即使使用诸如二进制搜索树（BST）之类的有效算法来实现解决方案，这也会增加存储这些区域和相关最佳控制律的系数所需的存储空间。所提出的方法旨在用一个多项式代替在可行状态空间的多个区域上定义的最优线性/仿射控制定律。多项式将是多元的，具有较高的阶数，并且是在可行区域的组合上定义的。使用多项式逼近，可以显着减少存储区域坐标和最佳控制律系数所需的存储空间。尽管使用多项式逼近会降低最佳控制器的精度，但它会使实现更加现实;只要问题的稳定性和可行性得到保证。

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