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Distributed Intelligent Learning and Decision Model Based on Logic Predictive Control.

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Summary
This summary is machine-generated.

This study addresses feedback delay in artificial intelligence (AI) sports education systems using model predictive control. Novel strategies improve system stability, efficient channel access, and energy savings in AI-driven physical education.

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Area of Science:

  • Sports Science
  • Artificial Intelligence
  • Control Systems Engineering

Background:

  • Integrating artificial intelligence (AI) into sports education presents challenges, particularly concerning feedback delay in intelligent learning systems.
  • Mobile communication, pattern recognition, and virtual technology offer avenues for innovative AI-driven online sports teaching and enhanced user experience.

Purpose of the Study:

  • To investigate and mitigate feedback delay in nonlinear multiagent systems within AI-powered sports learning environments.
  • To develop efficient and energy-saving channel access strategies for intelligent sports learning systems.

Main Methods:

  • Employed documentation and logical analysis of AI in sports education, focusing on machine learning for activity identification and intelligent decision support for talent recognition.
  • Utilized consistent model predictive control (MPC) for feedback delay in nonlinear multiagent systems with network-induced delay and random communication protocols.
  • Proposed a dual-channel awareness scheduling strategy within an MPC framework, incorporating distributed sensor thresholds and priority controller thresholds.

Main Results:

  • A communication waiting mechanism was designed, granting agents tolerance to delay while ensuring system stability.
  • A random communication protocol was developed to maintain ordered communication within the multiagent system.
  • The proposed dual-channel awareness scheduling strategy demonstrated efficient channel access and energy savings, with sensor networks reaching a Nash equilibrium and controller priority strategies outperforming traditional methods.

Conclusions:

  • The developed methods effectively address feedback delay and channel competition in AI-based sports learning systems.
  • The integration of AI in physical education (PE) holds significant potential for personalized learning, talent identification, and efficient teaching evaluations.
  • The proposed control strategies enhance the stability, efficiency, and energy conservation of intelligent sports learning systems.