Electric pumps are widely used in applications such as sanitation, manufacturing and agriculture. Electric current is supplied to the pumps, which translates into a corresponding flow rate and therefore output pressure. This relationship between a pump’s pressure and flow rate is described as its performance curve. This conference paper uses estimation theory and cognitive system techniques to improve the efficiency of electric pumps. Specifically, using the perception-action cycle to observe the states, predict the system behaviour and then optimize it. The system states are estimated using sensor measurements and system dynamics, where the control system uses the states to find the optimal flow rate based on the performance curve and adjust the system accordingly. This methodology is validated using simulations. The simulation models a sprayer that is powered by a DC motor where the ideal spray angle is maintained based on the distance to the surface. Optimizing the electric pump performance, reduces energy consumption and optimizes fluid usage, which can provide savings in many industries and systems.
Unmanned aerial vehicles (UAV) and satellites are becoming increasingly popular in business, government, and military applications. Both have unique use cases and value, but they have several overlapping use cases and features. Most notably they are both used for observation, such as the case of climate monitoring or surveying and mapping. Satellites also have uses in communication and navigation by broadcasting signals and enabling technology such as global positioning systems (GPS). UAVs have also been deployed by the militaries across the world for both reconnaissance and offensive capabilities. Each are electro-mechanical systems with a several important components that need to be reliable and high performance. Maximizing the return in value for these assets might mean improving their performance, reliability, or longevity. One emerging technology that has the promise to do this is the digital twin (DT). DTs utilize a combination of multi-domain modeling and extensive data collection for real-time model updates. This real time updating can be utilized for advanced simulation, improved control, and advanced condition monitoring. DTs are an ideal platform for applying to UAVs and satellites to maximize their capabilities and values. As will be demonstrated in this work, DTs have been demonstrated to provide value in improving control performance, orientation and position tracking, condition monitoring, and fault detection in UAVs and satellites. A case study and preliminary work on a CubeSat attitude adjustment device DT has been presented and examined to display benefits of the concept.
In this article, the power of physics-informed neural networks is employed to address the issue of model identification for complex physical systems, focusing on the application of a magnetorheological (MR) damper setup. The research leverages the Bouc-Wen hysteresis model, a well-established representation of nonlinear behavior in MR dampers, to inform the training process of a series of cascaded neural networks. The core objective of this research is to develop a surrogate model capable of accurately predicting the dynamic behavior of MR dampers under various operational conditions. Traditionally, MR dampers pose significant modelling challenges due to their nonlinear and hysteresis-rich characteristics. The approach explored in this article combines physics-based insights with the capabilities of neural networks to resolve the complexity associated with the modelling process. The methodology involves the formulation of a physics-informed loss function, which embeds the Bouc-Wen hysteresis model’s governing equations into the training process of the neural networks. This fusion of physical principles and machine learning enables the networks to inherently capture the underlying physics, resulting in a more accurate and interpretable surrogate model. Through experimentation, the effectiveness of the physics-informed neural network approach in surrogate modeling for MR dampers is demonstrated. The model developed exhibits decent predictive performance across a range of input parameters and excitation conditions, offering a promising alternative to conventional black-box machine learning and physics-based methods. Furthermore, this research showcases the potential for physics-informed machine learning in modelling complex physical systems, offering a perspective on the utility of this approach in other engineering and scientific domains. The application of this methodology further facilitates improved control and optimization strategies in various engineering applications.
This paper details the design, fabrication, and development of an improved Nanosatellite Attitude Control Simulator (NACS). The NACS consists of a mock 1U CubeSat (MockSat), tabletop air-bearing, and automatic balancing system (ABS). The MockSat employs a reaction wheel array to exchange momentum with the rigidlyattached air bearing platform, and an inertial measurement unit to obtain orientation and angular velocity estimates. The ABS tunes the Simulator’s center of gravity to coincide with the air bearing’s center of rotation in an effort to minimize gravitational torques. This paper presents the majority of the mechanical design process, as well as future insights into the ABS control system. The NACS will be used to build numerous data sets for the development and training of new machine learning algorithms, as well as to benchmark, test, and compare different estimation and control strategies.
Earth observation satellites, such as those responsible for monitoring the effects of climate change, require rigorous calibration protocols to account for on-orbit sensor degradation. An increasingly dependable method to address this issue uses the Moon as a reference light source for in-situ calibration. The airborne lunar spectral irradiance (air-LUSI) mission aims to improve the utility of the Moon as an on-orbit calibration target for remote sensing instruments, by tying the currently accepted lunar model to the SI and establishing lunar irradiance on an absolute scale. To this end, air-LUSI collects SI-traceable measurements of lunar irradiance at visible to nearinfrared wavelengths with unprecedented accuracy. A non-imaging telescope is flown at an altitude of 21 km, aboard NASA’s high-altitude ER-2 aircraft, which places the instrument above 95% of the Earth’s atmosphere for clean, minimally obstructed lunar spectra. To fix the optical axis on the Moon during flight, an autonomous control system is required to compensate for aircraft motion and track the Moon across its celestial transit. In this paper, we present an overview of the robotic subsystem used to track the Moon on more than ten high-altitude flights, and the valuable lessons learned from those campaigns. From this insight, a preliminary design for a second-generation robotic telescope mount is presented. Referred to as the HAAMR, it will supplant the current robotics system on future air-LUSI Operational Flight Campaigns, with the nearest field deployment slated for January 2024. We show how this new system is poised to offer a more reliable, accurate, and responsive platform for the air-LUSI instrument to continue collecting data that will ultimately help to improve our understanding of the Earth’s climate.
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