Data-driven tuning is an alternative to model-based controller design where controllers are directly identified from data, avoiding a plant identification step. In this paper, an approach to tune limited-complexity controllers from data for linear systems is proposed. The controller is parametrized as a linear combination of a large set of basis functions and the proposed algorithm allows to select a sparse subset of bases, guaranteeing a bounded approximation error. A feasibility condition allows to adjust the trade-off between accuracy and sparsity. The controller design is performed by solving a set of linear programming problems, allowing to handle large data-sets. The proposed strategy is evaluated by means of a Monte-Carlo simulation experiment on a flexible transmission benchmark model. Results show that the proposed solution offers similar results than previous approaches for large data-sets, requiring less adjustable parameters. However, for reduced data-sets, the presented algorithm shows better performance than the compared approaches.

数据驱动的调整是基于模型的控制器设计的替代方法，在该模型中，直接从数据中识别出控制器，从而避免了工厂识别步骤。本文提出了一种从线性系统的数据中调优有限复杂度控制器的方法。控制器被参数化为一大套基本函数的线性组合，并且所提出的算法允许选择稀疏的基本子集，从而保证了有限的近似误差。可行性条件允许调整准确性和稀疏性之间的权衡。通过解决一组线性编程问题来执行控制器设计，从而可以处理大型数据集。拟议的策略是通过在灵活的变速箱基准模型上进行的蒙特卡洛模拟实验评估的。结果表明，对于大数据集，所提出的解决方案与以前的方法提供了相似的结果，所需的可调参数更少。然而，对于减少的数据集，与比较方法相比，所提出的算法表现出更好的性能。