This paper presents a two-step optimization-based design method for iterative learning control and applies it onto the quad-rotor unmanned aerial vehicle's (UAV's) trajectory tracking problem. Iterative learning control aims to improve the tracking performance through learning from errors over iterations in repetitively operated systems. The tracking errors from previous iterations are injected into a learning filter and a robust filter to generate the learning signal. The design of the two filters usually involves non-trivial tuning work. This paper presents a new two-optimization design method for the iterative learning control, which is easy to obtain and implement. In particular, the learning filter design problem is transferred into a feedback controller design problem for a purposely constructed system, which is solved based on H-infinity optimal control theory thereafter. The robust filter is then obtained by solving an additional optimization to guarantee the learning convergence. Through the proposed design method, the learning performance is optimized and the system's stability is guaranteed. The proposed two-step optimization-based design method and the regarding iterative learning control algorithm are validated by both numerical and experimental studies.

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Unmanned aerial vehicles, specifically quadrotor drones, are increasingly commonplace in community and workplace settings and are often used for photography, cinematography, and small parcel transport. The presence of these flying robotic systems has a substantial impact on the surrounding environment. To better understand the ergonomic impacts of quadrotor drones, a quantitative description of their acoustic signature is needed. While previous efforts have presented detailed acoustic characterizations, there is a distinct lack of high spatial-fidelity investigations of the acoustic field of a quadrotor hovering under its own power. This work presents an experimental quantification of the spatial acoustic pressure distribution in the near-field of a live hovering unmanned aerial vehicle. A large-aperture scanning microphone array was constructed to measure sound pressure level at a total of 1728 points over a 2 m × 3 m × 1.5 m volume. A physics-infused machine learning model was fit to the data to better visualize and understand the experimental results. The experimental data and modeling presented in this work are intended to inform future design of experiments for quadrotor drone acoustics, provide quantitative information on the acoustic near-field signature, and demonstrate the utility of optical motion tracking coupled with a custom microphone array for characterization of live acoustic sources.

Acoustic phased arrays are capable of steering and focusing a beam of sound via selective coordination of the spatial distribution of phase angles between multiple sound emitters. Constrained by the principle of reciprocity, conventional phased arrays exhibit identical transmission and reception patterns which limit the scope of their operation. This work presents a controllable space–time acoustic phased array which breaks time-reversal symmetry, and enables phononic transition in both momentum and energy spaces. By leveraging a dynamic phase modulation, the proposed linear phased array is no longer bound by the acoustic reciprocity, and supports asymmetric transmission and reception patterns that can be tuned independently at multiple channels. A foundational framework is developed to characterize and interpret the emergent nonreciprocal phenomena and is later validated against benchmark numerical experiments. The new phased array selectively alters the directional and frequency content of the incident signal and imparts a frequency conversion between different wave fields, which is further analyzed as a function of the imposed modulation. The space–time acoustic phased array enables unprecedented control over sound waves in a variety of applications ranging from ultrasonic imaging to non-destructive testing and underwater SONAR telecommunication.

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Chemical energy ferroelectrics are generally solid macromolecules showing spontaneous polarization and chemical bonding energy. These materials still suffer drawbacks, including the limited control of energy release rate, and thermal decomposition energy well below total chemical energy. To overcome these drawbacks, we report the integrated molecular ferroelectric and energetic material from machine learning-directed additive manufacturing coupled with the ice-templating assembly. The resultant aligned porous architecture shows a low density of 0.35 g cm−3, polarization-controlled energy release, and an anisotropic thermal conductivity ratio of 15. Thermal analysis suggests that the chlorine radicals react with macromolecules enabling a large exothermic enthalpy of reaction (6180 kJ kg−1). In addition, the estimated detonation velocity of molecular ferroelectrics can be tuned from 6.69 ± 0.21 to 7.79 ± 0.25 km s−1 by switching the polarization state. These results provide a pathway toward spatially programmed energetic ferroelectrics for controlled energy release rates.

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This work presents a framework aimed at mitigating adverse effects of high-amplitude drone noise ranging from hearing loss to reduced productivity in human–robot collaborative environments by infusing acoustic awareness in a path planning algorithm without imposing any additional design layers or hardware to an operational drone. Following a detailed outline of the proposed approach, it is shown that a significant reduction of noise levels perceived by human workers at noise-sensitive locations is realized via a path planner which generates optimal paths ranging from quietest to shortest paths. The approach is then augmented with a path-correction mechanism which accounts for noise exposure duration to ensure the aforementioned optimal paths are compliant with a given industrial/environmental standard. The correction mechanism enforces an adjustment of subsets of the planned paths inside quiet zones designated around noise-sensitive locations. The presented concepts were verified using numerical simulations conducted for a 2-dimensional rasterized obstacle field followed by a statistical design of experiments. The proposed framework is highly versatile and integrable with widely used industrial path planners, rendering it a highly valuable tool for noisy collaborative workplaces.