A convex optimization approach to synthesizing state feedback data-driven controllers for switched linear systems
A convex optimization approach to synthesizing state feedback data-driven controllers for switched linear systems
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DOI：
10.1016/j.automatica.2022.110190
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
T. Dai;M. Sznaier
中科院分区：
文献类型：
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作者：
T. Dai;M. Sznaier
本文旨在开发一个计算上易于处理的数据驱动控制切换线性系统的框架。具体来说，给定一个模型结构和实验数据收集在不同的操作点，我们试图直接设计一个状态反馈控制器，稳定的系统，任意切换之间的所有子系统，可能产生的观测数据，没有明确的植物识别步骤。本文的主要结果表明，这个鲁棒优化问题可以重铸，通过使用对偶，多项式优化的形式和有效地解决，导致一个鲁棒控制器具有保证的最坏情况下的性能。所提出的技术的有效性说明了几个例子，包括控制的水平运动的四轴飞行器
This paper seeks to develop a computationally tractable framework for data-driven control of switched linear systems. Specifically, given a model structure and experimental data collected at different operating points, we seek to directly design a state-feedback controller that stabilizes a system that arbitrarily switches amongst all sub-systems that could have generated the observed data, without an explicit plant identification step. The main result of the paper shows that this robust optimization problem can be recast, through the use of duality, into a polynomial optimization form and efficiently solved, leading to a robust controller with guaranteed ℓ∞ worst-case performance. The effectiveness of the proposed technique is illustrated with several examples, including control of the horizontal motion of a quadcopter