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A minimum attention control law for ball catching.

Cheongjae Jang1, Jee-eun Lee, Sohee Lee

  • 1Robotics Laboratory, Seoul National University, Seoul, Korea.

Bioinspiration & Biomimetics
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Summary
This summary is machine-generated.

This study introduces an attention-minimizing control law for tasks like ball catching. The new control law reduces computational demands and improves robustness in digital implementations, inspired by human motor control principles.

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Area of Science:

  • Robotics and Control Systems
  • Computational Neuroscience
  • Applied Mathematics

Background:

  • Digital control systems often require high levels of discretization, increasing computational load and implementation costs.
  • Quantifying 'control attention' is crucial for optimizing control laws, with rate of change concerning state and time being a key metric.
  • Existing control strategies may not adequately address efficiency and robustness in discretized environments.

Purpose of the Study:

  • To develop and present an attention-minimizing control law for target tracking tasks, specifically ball catching.
  • To quantitatively measure and minimize the 'attention' of a control law using Brockett's attention criterion.
  • To explore the connection between this attention criterion and established principles in human motor control.

Main Methods:

  • Derivation of a Linear Quadratic Regulator (LQR)-based minimum attention tracking control law.
  • Assumption of optimal control as a sum of linear time-varying feedback and time-varying feedforward terms.
  • Efficient optimization over symmetric positive-definite matrices for control law computation.

Main Results:

  • The derived control law is stable and efficient to compute.
  • Numerical experiments on ball catching demonstrated familiar human motor control features, like open-loop to closed-loop transitions.
  • The attention-minimizing control law showed improved robustness against spatiotemporal discretization.

Conclusions:

  • The developed control law effectively minimizes attention, reducing implementation costs and enhancing robustness.
  • The findings align with theories of human motor control in tasks like ball catching.
  • The control laws are generalizable to various tracking problems, especially those with limited communication resources.