The development of intelligent control-oriented solutions for building energy systems is a promising research field. The development of effective systems relies on seldom available large data sets or on simulation environments, either for training or execution phases. The creation of simulation environments based on thermal models is a challenging task, requiring the usage of third-party solutions and high levels of expertise in the energy engineering field, which poses relevant restrictions to the development of control-oriented research.

In this work, a training workbench is presented, integrating an accurate but lightweight lumped capacitance model with proven accuracy to represent the thermal dynamics of buildings, engineering models for energy systems in buildings, and user behavior models into an overall building energy performance forecasting model. It is developed in such a way that it can be easily integrated into control-oriented applications, with no requirements to use complex, third-party tools.

Membrane aerofoils are used for the design of small unmanned air vehicles which have gained interest in the past few years. This paper deals with the nonlinear uncertain aeroelastic analysis of an elastically supported membrane aerofoil. The uncertainties in the aerofoil aerodynamic coefficients are estimated due to five uncertain input parameters, which are the initial tension coefficient, the membrane elastic modulus, the stiffness coefficients of the two supporting springs at the trailing edge and the leading edge, and the fifth parameter is the free stream angle-of-attack. Both static uncertain aeroelasticity and dynamic aeroelasticity for a sinusoidal gust loading are considered. A detailed novel parametric analysis is performed to assess the effect of each parameter. The analysis is carried out using a nonlinear aeroelastic finite element method, which is based on the Theodorsen’s unsteady aerodynamics theory. The polynomial chaos expansion method is used for the uncertainty quantification process and for the sensitivity analysis. Also, the Karhunen-Loéve expansion is used to model the random field of the elastic modulus. The interesting results of the analysis show that the effect of each uncertain input depends on the values of the other parameters and that the initial tension is the key parameter. The type of the probability density functions (or histograms) of the aerodynamic coefficients can vary from a Gaussian distribution to an exponential-like distribution.

The rapid growth of civil aviation has posed significant challenges to air traffic management (ATM), highlighting the need for accurate aircraft trajectory prediction (TP). Due to the scarcity of relevant data and the resulting class imbalance in the sample, aircraft TP under severe weather conditions faces significant challenges. This paper proposes an aircraft TP method framework consisting of trajectory data augmentation and TP networks to address this issue. To validate the effectiveness of this framework in solving the TP problem in severe weather, we propose an improved conditional tabular generative adversarial networks (CTGAN)-long short-term memories (LSTMs) hybrid model. We conduct comparative experiments of four LSTM-based models (LSTM, convolutional neural network (CNN)-LSTM, CNN-LSTM-attention, and CNN-BiLSTM) under this framework. The improved CTGAN is also compared with the commonly used data augmentation method, the Synthetic Minority Oversampling Technique (SMOTE). The results show that the TP accuracy can be effectively improved by enhancing the minority-class sample data; compared with SMOTE, the improved CTGAN is more suitable for minority-class sample data augmentation for aircraft TP, and it also shows that for minority-class sample data augmentation, data distribution characteristics are more important than the simple trajectory point accuracy. The hybrid modeling approach with the improved CTGAN as the data augmentation network proposed in this study provides valuable insights into addressing the data imbalance problem in aircraft TP.

Aeroelastic analyses are part of the design of modern propeller blades. Most of the time, advanced numerical simulations are used, involving computational fluid dynamics. However, the coupling between fluid and structure may be missing. In this paper, two coupled fluid-structure interaction methods are presented, namely the modal time-marching and the quasi-static approach. An in-house aeroelastic tool, analysing an in-house blade design, is used. A limited number of experiments are available, and this was alleviated using new experiments as part of the Numerical and Experimental Study of Propeller Aeroelasticity project. In this work, 3D finite element models (FEM) were used to represent the blade structure. Time-marching and quasi-steady results were compared, and this is the first time that this is reported in the literature. It was found that regardless of the differences in the aerodynamic loads between time-marching and quasi-static computations, the final blade deformations were comparable. Time-marching computations using a modal representation of the blade, obtained from 3D FEM, showed that the blade deformation and vibration were driven by the stalled flow. This observation was verified by comparing the blade response with the flow off-the-blade. The harmonic content of the results includes the propeller blade passing frequency and its natural frequencies, but also additional frequencies related to the flow shedding and vortical content of the stalled part of the blade. To the best of our knowledge, this has not been reported in the open literature.

System uncertainty remains a challenge for effective control of lower extremity exoskeletons, particularly in clinical populations. Adaptive control offers a potential solution by accounting for unknown system characteristics in real time. Here, we introduce the use of Gaussian-based adaptive control (GBAC) in a two-degree-of-freedom (DOF) exoskeleton for an angular position tracking task in the presence of system uncertainty. The mathematical derivation of the implicitly non-Lyapunov adaptation law is presented using Lagrangian mechanics, including a Gaussian kernel regressor and its stable convergence. We then evaluate GBAC performance in a 2-DOF simulation compared with a previously developed robust adaptive backstepping algorithm, Lyapunov-stable Slotine–Li control, and a proportional-integral-derivative (PID) controller. We additionally complete 1-DOF simulations to evaluate the effects of external disturbance and parameter uncertainty on controller performance. Finally, we evaluate GBAC experimentally in our existing 1-DOF knee exoskeleton along with Slotine–Li and PID controllers. The simulation results demonstrate the improved tracking performance and faster convergence of GBAC, especially in the presence of an external disturbance and uncertainty introduced by extra segment length and mass. The experimental results demonstrate similar performance, wherein GBAC and Slotine–Li provide stable tracking in the presence of unmodeled system dynamics; however, convergence time was faster and tracking error was lower for GBAC. Collectively, these results demonstrate that GBAC is an effective adaptive controller in the presence of system uncertainty and therefore warrants further development and investigation for use in flexible joint exoskeleton systems, particularly those designed for pediatric and/or clinical populations that have inherently high uncertainty.

This paper presents a comprehensive approach for mitigating noise pollution from unmanned aerial vehicles (UAVs) in urban environment through path planning using reinforcement learning (RL). The study focuses on Turin, Italy, leveraging its diverse urban architecture to develop a comprehensive model. A detailed 3D occupancy grid map, based on OpenStreetMap data, was created to represent buildings’ locations and heights while a population density map was developed to account for demographic variances. The research develops a dynamic noise source model that adjusts noise emission levels based on UAV velocity, ensuring realistic noise impact predictions. Acoustic ray tracing techniques are utilised to simulate noise propagation, accounting for atmospheric absorption and reflections from urban structures, providing a detailed analysis of noise distribution. The core of this work is the application of the deep deterministic policy gradient (DDPG) algorithm within the RL framework. The algorithm is tailored to optimise flight paths by minimising noise impact while balancing other factors like path length and energy efficiency. The RL agent learns to navigate complex urban landscapes, integrating penalties for idling, excessive path length and abrupt manoeuvers to refine its path planning strategy. Simulation results with several maps unseen during training reveal that the RL-based approach effectively reduces noise impact in urban settings, making it a viable solution for better integrating UAVs into urban air mobility (UAM) systems. The methodology is scalable and adaptable, with potential applications in various urban environments globally. This research contributes to the development of sustainable drone operations in UAM context by addressing the critical issue of noise pollution, enhancing public acceptance and regulatory compliance.

Vibration is defined as oscillation away from equilibrium and is a significant problem in aviation. Vibration is transmitted to the flight crew through all contact surfaces, including flight controls, floor and seats. Various effects are known to occur on flight crew exposed to vibration, with fatigue and low back pain being the most common vibration-related health complaints. Studies have shown that prolonged exposure to vibration or accumulated vibration can increase the risk of chronic low back pain and injury by increasing the exposure dose. In particular, there is data that helicopter pilots have more low back pain than fixed wing pilots. This is due to the fact that helicopters have much more vibration-generating factors due to the working principle of helicopters and the posture of helicopter pilots is slightly forward-leaning. In this study, an isolator cushion with a quasi-zero stiffness mechanism was developed, manufactured and tested to reduce the transmission of vibration to the pilot and flight crew during the operation of the Sikorsky UH-60 helicopter. The use of the specially designed cushion led to a noticeable reduction in vibration exposure under various flight conditions.

We present a proof-of-principle study of active beam-pointing control for the Zettawatt-Equivalent Ultrashort pulse laser System (ZEUS) using a piezo-actuated 16-inch mirror. To the best of our knowledge, this is the largest actively controlled mirror reported in a high-power laser system. A simple proportional feedback control was implemented based on a field-programmable gate array, which reduced the standard deviation of beam-pointing fluctuations by 91% to 0.075 μrad in the horizontal direction and by 78% to 0.25 μrad in the vertical direction. We also demonstrated the elimination of long-term pointing jitter caused by temperature drift using the same apparatus.

This paper presents a detailed technical overview of the femtosecond precision timing and synchronization systems implemented at the Shanghai high repetition rate XFEL and extreme light facility (SHINE). These systems are designed to deliver stabilized optical references to multiple receiver clients, ensuring high-precision synchronization between the optical master oscillator (OMO) and optical/RF subsystems. The core components include an OMO, fiber length stabilizers and laser-to-laser synchronization modules that achieve femtosecond-level accuracy. Our discussion extends to the various subsystems that comprise the synchronization infrastructure, including the OMO, fiber length stabilizer and advanced phase detection techniques. Finally, we highlight ongoing research and development efforts aimed at enhancing the functionality and efficiency of these systems, thereby contributing to the advancement of X-ray free-electron laser technology and its applications in scientific research.

This study investigates the incorporation of advanced heating, ventilation, and air conditioning (HVAC) systems with reinforcement learning (RL) control to enhance energy efficiency in low-energy buildings amid the extreme seasonal temperatures of Tehran. We conducted comprehensive simulation assessments using the EnergyPlus and HoneybeeGym platforms to evaluate two distinct reinforcement learning models: traditional Q-learning (Model A) and deep reinforcement learning (DRL) with neural networks (Model B). Model B consisted of a deep convolutional network architecture with 256 neurons in each hidden layer, employing rectified linear units as activation functions and the Adam optimizer at a learning rate of 0.001. The results demonstrated that the RL-managed systems resulted in a statistically significant reduction in energy-use intensity of 25 percent (p < 0.001), decreasing from 250 to 200 kWh/m² annually in comparison to the baseline scenario. The thermal comfort showed notable improvements, with the expected mean vote adjusting to 0.25, which falls within the ASHRAE Standard 55 comfort range, and the percentage of anticipated dissatisfaction reduced to 10%. Model B (DRL) demonstrated a 50 percent improvement in prediction accuracy over Model A, with a mean absolute error of 0.579366 compared to 1.140008 and a root mean square error of 0.689770 versus 1.408069. This indicates enhanced adaptability to consistent daily trends and irregular periodicities, such as weather patterns. The proposed reinforcement learning method achieved energy savings of 10–15 percent compared to both rule-based and model predictive control and approximately 10 percent improvement over rule-based control, while employing fewer building features than existing state-of-the-art control systems.