Human Control Model Estimation in Physical Human-Machine Interaction: A Survey

Area of Science:

• Engineering
• Control Systems
• Human-Machine Interaction

Background:

• Human-machine interaction (HMI) has been a foundational engineering research area for nearly a century.
• It remains critical for advancements in collaborative robotics and artificial intelligence.
• Understanding human control strategies in HMI is vital across neuroscience, aerospace, robotics, and AI.

Purpose of the Study:

• To provide a comprehensive overview of linear models for human-machine control systems.
• To unify diverse modeling approaches in motor control, physiological systems, and HMI.
• To facilitate the analysis of human-machine control models in manipulative tasks.

Main Methods:

• Review and synthesis of existing linear control models for HMI.
• Discussion of models ranging from early quasi-linear frequency domain to optimal control models.
• Focus on methodologies from classical linear systems and control theory.

Main Results:

• Identified challenges in integrating motor control theory with physiological and HMI models.
• Detailed various linear models, including those with biomechanical and physiological subsystems.
• Highlighted the utility of linear models for both time and frequency domain analysis.

Conclusions:

• Linear models offer a tractable framework for understanding human-machine control.
• A unified perspective on these models aids research across multiple domains.
• The presented methodology supports robust analysis in classical control theory.