A Novel Adaptive Controller for Robot Manipulators Based on Active Inference

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Abstract

More adaptive controllers for robot manipulators are needed, which can deal with large model uncertainties. This letter presents a novel active inference controller (AIC) as an adaptive control scheme for industrial robots. This scheme is easily scalable to high degrees-of-freedom, and it maintains high performance even in the presence of large unmodeled dynamics. The proposed method is based on active inference, a promising neuroscientific theory of the brain, which describes a biologically plausible algorithm for perception and action. In this work, we formulate active inference from a control perspective, deriving a model-free control law which is less sensitive to unmodeled dynamics. The performance and the adaptive properties of the algorithm are compared to a state-of-the-art model reference adaptive controller (MRAC) in an experimental setup with a real 7-DOF robot arm. The results showed that the AIC outperformed the MRAC in terms of adaptability, providing a more general control law. This confirmed the relevance of active inference for robot control.

Original languageEnglish
Pages (from-to)2973-2980
JournalIEEE Robotics and Automation Letters
Volume5
Issue number2
DOIs
Publication statusPublished - 2020

Keywords

  • Biologically-inspired robots
  • adaptive control of robotic systems
  • industrial robots
  • active inference
  • free-energy principle

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