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Machine learning control
(2020)
Recently, the term explainable AI came into discussion as an approach to produce models from artificial intelligence which allow interpretation. For a long time, symbolic regression has been used to produce explainable and mathematically tractable models. In this contribution, we extend previous work on symbolic regression methods to infer the optimal control of a dynamical system given one or several optimization criteria, or cost functions. In earlier publications, network control was achieved by automated machine learning control using genetic programming. Here, we focus on the subsequent path continuation analysis of the mathematical expressions which result from the machine learning model. In particular, we use AUTO to analyze the solution properties of the controlled oscillator system which served as our model. As a result, we show that there is a considerable advantage of explainable symbolic regression models over less accessible neural networks. In particular, the roadmap of future works may be to integrate such analyses into the optimization loop itself to filter out robust solutions by construction.
Many previous studies have shown that the turbulent mixing layer under periodic forcing tends to adopt a lock-on state, where the major portion of the fluctuations in the flow are synchronized at the forcing frequency. The goal of this experimental study is to apply closed-loop control in order to provoke the lock-on state, using information from the flow itself. We aim to determine the range of frequencies for which the closed-loop control can establish the lock-on, and what mechanisms are contributing to the selection of a feedback frequency. In order to expand the solution space for optimal closed-loop control laws, we use the genetic programming control (CPC) framework. The best closed-loop control laws obtained by CPC are analysed along with the associated physical mechanisms in the mixing layer flow. The resulting closed-loop control significantly outperforms open-loop forcing in terms of robustness to changes in the free-stream velocities. In addition, the selection of feedback frequencies is not locked to the most amplified local mode, but rather a range of frequencies around it.
Mixing layer manipulation experiment from open-loop forcing to closed-loop machine learning control
(2015)
Ecosystems with highly pulsed water supply must be better understood as climate change may increase frequency and severity of intense storms, droughts and floods. Here we collected data over 3 years (2016-2018) in the episodic wetland outflow channel (Aluize), Banhine National Park, in which the system state changed from dry to wet to dry. Field sampling included vegetation records, small-scale vegetation zoning, the seed bank and water and soil quality. The same main plant species were found in both dry and wet conditions across the riverbed of the outflow channel. We found only very few diaspores of plants in the soil after prolonged drought. In the subsequent flooded state, we examined very dense vegetation on the water surface, which was dominated by the gramineous species Paspalidium obtusifolium. This species formed a compact floating mat that was rooted to the riverbed. The Cyperaceae Bolboschoenus glaucus showed high clonal growth in the form of root tubers, which likely serve as important food reservoir during drought. Soil and water analyses do not indicate a limitation by nutrients. We outline how resident people may change the plant community structure with an increasing practice of setting fire to the meadows in the dried-up riverbed to facilitate plant regrowth as food for their livestock.