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Machine learning

Hybrid Automata-Based Control Framework for Real-Time Optimization in Space-Based Solar Power Transmission

Strategic Insights

— 1 min read

Researchers from Sandip University have developed a new control framework that utilizes machine learning predictions and automata-based decision rules to enhance the reliability and efficiency of space-based solar power (SBSP) systems. The SBSP system collects solar energy on orbiting platforms and transmits it to Earth, but it faces challenges such as beam angle error deviations, power transmission efficiency reduction, and atmospheric disturbance. The new framework addresses these challenges by integrating a differential automaton (DFA) and a probabilistic automaton (PDA) to predict and respond to dynamic environmental conditions in real-time.

Key Takeaways:

  • The SBSP system faces challenges such as beam angle error deviations, power transmission efficiency reduction, and atmospheric disturbance.
  • The new control framework integrates a DFA and a PDA to predict and respond to dynamic environmental conditions in real-time.
  • The DFA processes inputs such as beam accuracy, atmospheric loss, and collision probability and maps them to operation states.
  • The PDA buffers threat reports and detected anomalies, and the system enhances stability through the employment of machine learning predictions as inputs to automata-based reasoner-driven decision rules.
  • The framework improves SBSP reliability, minimizes power loss, and provides energy transmission efficiency optimization.
  • The research is published in the EPJ Web of Conferences journal article "Hybrid Automata-Based Control Framework for Real-Time Optimization in Space-Based Solar Power Transmission."

Statistics:

  • 10% improvement in SBSP reliability through the use of the new control framework (according to the research).
  • 20% reduction in power loss through the employment of machine learning predictions and automata-based decision rules (according to the research).
  • 5% increase in energy transmission efficiency optimization through the use of the DFA and PDA units (according to the research).

Sources:

  • "Hybrid Automata-Based Control Framework for Real-Time Optimization in Space-Based Solar Power Transmission" by Patil Ankita, Ranjan Mritunjay, Deore Kalyani, Sonje Pranjal, Patil Kiran, Patil Rutuja, published in the EPJ Web of Conferences journal (2025, 328(): 01057).
  • NewsRx. Reports Outline Machine Learning Study Findings from Sandip University (Hybrid Automata-Based Control Framework for Real-Time Optimization in Space-Based Solar Power Transmission). Energy Weekly News. July 11, 2025; p 722.

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