CRL2RT combines classical controllers with RL in a time-interleaved Cloud-Edge design, reporting over 2500 Hz online-update control on CPUs and tracking gains of 18.3% to 60.7% in simulation.
Experimental study on the effect of increased downstroke duration for an FWAV with morphing-coupled wing flapping configuration,
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Real Time Control of Tandem-Wing Experimental Platform Using Concerto Reinforcement Learning
CRL2RT combines classical controllers with RL in a time-interleaved Cloud-Edge design, reporting over 2500 Hz online-update control on CPUs and tracking gains of 18.3% to 60.7% in simulation.