Rosafalco Luca, De Ponti Jacopo Maria, Iorio Luca, Craster Richard V, Ardito Raffaele, Corigliano Alberto
Department of Civil and Environmental Engineering, Politecnico di Milano, p.za L. da Vinci 32, 20133, Milano, Italy.
Department of Mathematics, Imperial College London, 180 Queen's Gate, SW7 2AZ, London, UK.
Sci Rep. 2023 Dec 9;13(1):21836. doi: 10.1038/s41598-023-48927-3.
The energy harvesting capability of a graded metamaterial is maximised via reinforcement learning (RL) under realistic excitations at the microscale. The metamaterial consists of a waveguide with a set of beam-like resonators of variable length, with piezoelectric patches, attached to it. The piezo-mechanical system is modelled through equivalent lumped parameters determined via a general impedance analysis. Realistic conditions are mimicked by considering either magnetic loading or random excitations, the latter scenario requiring the enhancement of the harvesting capability for a class of forcing terms with similar but different frequency content. The RL-based optimisation is empowered by using the physical understanding of wave propagation in a such local resonance system to constrain the state representation and the action space. The procedure outcomes are compared against grading rules optimised through genetic algorithms. While genetic algorithms are more effective in the deterministic setting featuring the application of magnetic loading, the proposed RL-based proves superior in the inherently stochastic setting of the random excitation scenario.
在微观尺度的实际激励下，通过强化学习（RL）可使渐变超材料的能量收集能力最大化。该超材料由一个带有一组长度可变的梁状谐振器的波导组成，这些谐振器上附着有压电片。通过一般阻抗分析确定的等效集总参数对压电器件系统进行建模。通过考虑磁负载或随机激励来模拟实际条件，后一种情况要求提高一类频率含量相似但不同的强迫项的收集能力。基于RL的优化通过利用对此类局部共振系统中波传播的物理理解来约束状态表示和动作空间。将该过程的结果与通过遗传算法优化的分级规则进行比较。虽然遗传算法在以磁负载应用为特征的确定性设置中更有效，但所提出的基于RL的方法在随机激励场景的固有随机设置中表现更优。
