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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Adaptive Athlete Training Plan Generation: An intelligent control systems approach.

Mark Connor¹, Marco Beato², Michael O'Neill³

¹Natural Computing Research and Applications Group, School of Business, University College Dublin, Ireland; School of Health and Sports Science, University of Suffolk, United Kingdom.

Journal of Science and Medicine in Sport

|November 12, 2021

Summary

An artificial intelligence system effectively optimizes team sport training plans by adapting to unexpected changes, significantly outperforming other control methods in simulations.

Keywords:

Artificial intelligence Control systems Decision support Evolutionary computation Planning Training load

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Area of Science:

Sports Science
Control System Theory
Artificial Intelligence

Background:

Effective planning and control of team sport training are crucial for athletic development and performance.
Unexpected disturbances can disrupt training plans, impacting outcomes.

Purpose of the Study:

To introduce a novel system using control theory and AI for optimal training plan construction.
To compare an AI-based controller with random and proportional controllers in adapting training loads.

Main Methods:

Formulated training load adaptation as an optimal control problem to minimize deviations from training goals.
Conducted 1800 computational simulations over a 60-day period using a non-linear training plan.
Assessed control strategies' ability to adapt future training loads following disturbances.

Main Results:

The artificial intelligence (AI) feedback controller significantly reduced deviations from training plan goals.
AI control outperformed random and proportional control strategies (p < .001, ES = 7.41).

Conclusions:

The developed system supports decision-making for effective athletic training planning and adaptation.
This AI-driven approach enhances the resilience of training plans to unforeseen events.