In this paper, a robust fault estimation approach is proposed for multi-input and multioutput nonlinear dynamic systems on the basis of back propagation neural networks. The augmented system approach, input-to-state stability theory, linear matrix inequality optimization, and neural network training/learning are integrated so that a robust simultaneous estimate of system states and actuator faults are achieved. The proposed approaches are finally applied to a 4.8 MW wind turbine benchmark system, and the effectiveness is well demonstrated.

In order to increase the efficiency of the solar photovoltaic (PV) system, accurate electrical modeling of the system under different environmental conditions is necessary. The double-diode electrical model of solar PV is known to be more accurate than its single diode model counterpart since it takes into account the effect of recombination. However, because of its nonlinear characteristics, the parameters of the double-diode model (DDM) have to be identified using optimization algorithms. In this paper, the wind-driven optimization (WDO) algorithm is proposed as a potential new method for identifying the parameters of a 12-parameter DDM of the solar PV. The accuracy and flexibility of the proposed method are verified using three different sets of data: first, experimental data at the controlled environmental condition; second, data sheet values of different solar PV modules; and third, real-time experimental data at the uncontrolled environmental condition. Additionally, the performance of the WDO is compared with other well-known existing optimization techniques. The obtained results show that the WDO algorithm can provide optimized values with reduced mean absolute error in power and reduced root mean square error for different types of solar PV modules at different environmental conditions. We show that the WDO can be confidently recommended as a reliable optimization algorithm for parameter estimation of solar PV model.

Versatile, in situ sensing and continuous monitoring capabilities are critically needed but challenging for components made of solid woven carbon fibers in aerospace, electronics and medical applications. In this work, we proposed a unique concept of integrated sensing technology on woven carbon fibers through integration of thin film surface acoustic wave (SAW) technology and electromagnetic metamaterials, with capabilities of non-invasive, in-situ and continuous monitoring of environmental parameters and biomolecules wirelessly. Firstly, we fabricated composite materials using a three-layer composite design, in which the woven carbon fiber cloth was firstly coated with a polyimide (PI) layer followed by a layer of ZnO film. Integrated SAW and metamaterials devices were then fabricated on this composite structure. Temperature of the functional area of the device can be controlled precisely using the SAW devices, which can provide a proper incubation environment for biosampling processes. As a demonstration for an ultraviolet light sensor, the SAW device could achieve a good sensitivity of 56.86 ppm/(mW∙cm-2). On the same integrated platform, the electromagnetic resonator based on the meta-materials has been demonstrated to work as a glucose concentration monitor with a sensitivity of 0.34 MHz/(mg/dL).

This paper examines the growing adoption of laser communication (lasercom) in space missions and payloads for identifying emerging trends and key technology drivers of future optical communications satellite systems. It also presents a comprehensive overview of commercially available and custom-designed lasercom terminals, outlining their characteristics and specifications to meet the evolving demands of global satellite networks. The analysis explores the technical considerations and challenges associated with integrating lasercom terminals into LEO constellations and the Inter-satellite communications service provision in LEO due to their power, size, and weight constraints. By analyzing advancements in CubeSat lasercom technology designed to cater for the emergence of future mega constellations of interacting small satellites, the paper underscores its promising role in establishing high-performance satellite communication networks for future space exploration and data transmission. In addition, a brief overview of our ALIGN planned mission is provided, which highlights the main key operational features in terms of PAT and link budget analysis.

A strain sensor based on cascaded Sagnac loops with two polarization-maintaining photonic crystal fibers (PMPCFs) has been experimentally demonstrated. Air holes distributed on the cross section of PMPCF go through the whole fiber, which makes the special fiber more sensitive to ambient force compared with conventional optical fiber. The lengths of the two PMPCFs are close, but not the same, to form envelopes on the total output spectrum and then obtain high resolution to the detected parameter by the Vernier effect. The influences of the difference in length of the two PMPCFS on spectral envelopes are discussed. As the length of the PMPCF in reference loop increases, free spectrum range of the cascaded Sagnac loops (or envelope period) and magnification decrease. The sensitivities of this strain sensor are calculated by tracking upper and lower envelopes. Results reveal that its average sensitivity is up to 45.15 pm/ $\mu \varepsilon $ and the corresponding resolution is $0.44~\mu \varepsilon $ as strain varies from 0 to $2000~\mu \varepsilon $ . Compared with a single Sagnac loop, the sensitivity magnification is about 28.94. The sensing characteristics of a conventional polarization-maintaining fiber (PMF) are studied to compare with the PMPCF. Moreover, reversibility has also been proved to possess low error rate. In short, the fiber strain sensor possesses high sensitivity and great reversibility.