Dynamics-based control offers a promising approach to exploring the motion potential of soft robots. However, inherently infinite degrees of freedom of these systems pose significant challenges for dynamics modeling, closely followed by the pressing robustness concerns arising from finite-dimensional approximations. This paper addresses these issues by proposing a physics-informed dynamics learning neural network and an adaptive fractional-order control for continuum soft robots. Specifically, a deep Lagrangian neural network is first developed with an embedded self-attention mechanism to enhance learning efficiency, accuracy, and data sensitivity. Subsequently, an adaptive fractional-order sliding mode controller is designed, leveraging the inherent historical memory properties of fractional calculus. This controller not only ensures robust shape control but also improves response speed and tracking accuracy. To further handle model discrepancies in the learned dynamics and external disturbances, a nonlinear disturbance observer is introduced to effectively estimate and compensate for lumped uncertainties, thereby ensuring reliable performance. Theoretical analysis confirms the closed-loop stability, while both simulation and experiment results validate the high dynamics fitting accuracy of the proposed network, as well as the robust and precise tracking capability of the fractional-order controller. Note to Practitioners-Soft robots offer great potential in unstructured or constrained environments owing to their compliance and adaptability. However, their high degrees of freedom and nonlinear behaviors make analytical modeling and robust control particularly challenging. Meanwhile, traditional closed-box learning methods often suffer from limited physical interpretability, reliability and extrapolability. This work presents a physics-informed dynamics learning framework combined with a fractional-order controller for soft robots. The dynamics learning network embeds physical priors to enhance model interpretability and extrapolability, while a self-attention mechanism improves data efficiency and modeling accuracy. Additionally, a disturbance observer is designed to estimate and compensate for model discrepancies and external disturbances, thereby contributing to the system's robustness. Incorporating the observer's outputs, the adaptive fractional-order controller further enhances closed-loop behavior by leveraging the memory properties of fractional calculus.
The long-term accumulation of halogenated organic compounds (HOCs) can cause severe environmental harm, and while anaerobic digestion is widely used to treat such wastewater, its efficiency is limited by electron transfer rate. This study synthesized a novel multivalent iron-modified biochar redox mediator (Fe0/Fen+-CRMs), which enhanced microorganism-pollutant electron transfer through multi-pathway mechanisms to promote halogenated organic pollutant degradation. Fe0/Fen+-CRMs were successfully loaded with Fe3O4 and ZVI on their surface, demonstrating excellent electrochemical performance. The addition of Fe0/Fen+-CRMs resulted in a 15-20% increase in the removal rate of HOCs and approximately a 10% rise in methane production compared to Fen+-CRMs and Fe0-CRMs alone. Moreover, stable and highly efficient dehalogenation performance was maintained throughout the entire experimental period. Furthermore, Fe0/Fen+-CRMs facilitated the enrichment of functional microorganisms (e.g. Georgenia). Research revealed that the electron transfer pathways of Fe0/Fen+- CRMs primarily include: (i) the conductive role of biochar's graphitic structure; (ii) electron donation by ZVI as a potent reducing agent; (iii) Fe (II/III) redox cycling. This study fabricated Fe0/Fen+-CRMs and proposed a multi-pathway electron transfer mechanism to accelerate microorganism-pollutant electron exchange, thereby alleviating the limiting effects of extracellular electron transfer on microbial anaerobic reductive dehalogenation efficiency, offering a viable strategy for enhancing halogenated organic pollutant removal in AD systems.
We present a novel continuous transverse stub (CTS) phased array with a hybrid electronical/mechanical beam steering for Ka-band satellite communication. The elevation angle scanning is controlled electrically whereas the azimuth angle scanning is achieved by rotating the array mechanically. Structurally, the two-dimensional (2-D) CTS phased array is composed of a set of one-dimensional (1-D) CTS arrays with a stable boresight radiation covering the Ka-band. To build the 1D CTS array, a 1 x 4 subarray is proposed and excited by a quasi-TEM mode implemented with the substrate integrated coaxial line (SICL). The parallel plate stub of CTS is designed in stepped form and a differential series-fed is also applied to the subarray for wide bandwidth and simplified feeding network. The 2-D CTS phased array can be flexibly scaled to the size with m x 4n elements based on the proposed subarray, and a 8 x 8 array is finally designed and analyzed. The simulated results show that an impedance bandwidth ranging from 26 to 31.2 GHz is obtained and a stable boresight radiation is also achieved from 27 GHz to 31 GHz with a peak gain of 21.4 dBi and gain variation within 1.7 dBi. A +/- 50 degrees beam scanning range is synthesized using the active element pattern method in the H-plane with a gain fluctuation within 4.2dBi.
