MATA-RL: Continuous Reaction Wheel Attitude Control Using the MATA Simulation Software and Reinforcement Learning

As earth observation satellites, Diwata microsatellites need to have a high degree of target pointing accuracy. Additionally, being in low orbit, they could experience strong external disturbances. Current methods for attitude control have proven to be effective. However, they are prone to changes in control and mass parameters. In this paper, we explore using Deep Reinforcement Learning (RL) for attitude control. This paper also leverages on Diwata’s simulator, MATA: Mission, Attitude, and Telemetry Analysis (MATA) software, in training the RL agent. We implemented two RL algorithms: Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). We then simulated different scenarios and compared the performance of these algorithms to that of Diwata’s current attitude controller, the Proportional-Integral-Derivative (PID) control. Our results show that reinforcement learning can outperform traditional controllers in terms of settling time, overshoot, and stability. The results of this research will help solve problems in conventional attitude controllers and enable satellite engineers to design a better Attitude Determination and Control System (ADCS)

Diwata-2: Earth Observation Microsatellite with a Compact Bus System, ElectronicallyTunable Multi-spectral Imager, and Amateur Radio Communications Capability

The microsatellite Diwata-2 was launched into the 600-km Sun-Synchronous Orbit (SSO) last October 29, 2018. It has a low-power, low-complexity, compact bus structure, capable of Earth observation and remote sensing mission through a 5-meter resolution Near-Infrared (NIR) High Precision Telescope (HPT) and a 125-meter resolution Space-borne Multispectral Imager (SMI) with two Liquid Crystal Tunable Filters (LCTF). The LCTF operates as an electronic-based band reconfiguration filter allowing for more than 600-channels of wavelength variation. As a secondary mission, Diwata-2 has full-duplex FM voice communications capability via a non-board module utilizing the amateur radio band at a 5W power requirement from mobile ground users. The structure has a 500-mm cubic external dimension, with JAXA’s Payload Attached Fairing (PAF) rocket interface and deployment mechanism. Deployable solar array panels (DSAP) were also introduced to increase the power generation capabilities of the microsatellite. The importance of detailed structural-mechanical models for finite-element analysis allowed for accurate structural simulation results. The observed accuracy is within 5-Hz for the first two modes compared to the actual vibration test results. Lastly, strict management of in-flight procedures allowed for consistent satellite performance, while modification of satellite maneuver based on imaging observation results improved target pointing accuracy to within 5-km

MATA-Cloud: A Cloud Detection and Dynamic Attitude Correction Evaluation Software

With the increasing demand for high-resolution images from earth observation satellites, there is a need to optimize the usability of the images being downloaded in the ground stations. Most captured satellite images are not usable for certain applications due to high cloud cover percentage. To address this problem, this research demonstrates a cloud detection and dynamic attitude correction evaluation software. This software explores two key experiments. First is evaluating different image processing and machine learning-based approaches to detect cloud cover. The cloud detection algorithms were evaluated based on their accuracy, latency, and memory consumption. The second is exploring dynamic attitude correction to minimize the effect of cloud cover on captured images. Results show that our software can help test algorithms that increase the usability of captured images

Growing the Local Space Workforce Through Synergistic Collaborations of the Philippine Space Agency, Universities, and Private Industry

For decades, space technology and applications development have been in the forefront of human advancement. To maximize the gains from these achievements, numerous countries have established space agencies to manage the growing space economy. However, for emerging space countries, the establishment of a space agency and a complementary space ecosystem proves to be a more complex and challenging task. In this paper, we present a review of lessons learned in building up the local upstream space workforce in the Philippines through various projects spearheaded by the government, mostly through the Philippine Space Agency (PhilSA). For the projects in collaboration with universities, this paper discusses the importance of providing training programs, scholarship opportunities, research and development activities, and promotion of current Space Science and Technology capabilities to create a young pool of knowledgeable personnel. On the other hand, collaborations with the local industry provide a support to ongoing satellite development activities in PhilSA. Established companies specializing in space-adjacent activities such as those in the manufacturing, electronics, and software development have immense potential in transitioning to actual space development activities. The paper highlights the lessons learned from PhilSA\u27s ongoing collaborations with these companies, and how such engagements translate to a more skilled space workforce. This paper summarizes the challenges faced, milestones achieved, and how the lessons learned are applied to the current activities in PhilSA and form strategic plans. These lessons learned can be helpful to other emerging space nations looking to ramp up capacity building and establish a thriving space ecosystem