Gaussian-Process-Based Control of Underactuated Balance Robots With Guaranteed Performance

Kuo Chen, Jingang Yi, Dezhen Song

Research output: Contribution to journalArticlepeer-review


The control of underactuated balance robots is aimed at performing both the external (actuated) subsystem trajectory tracking and internal (unactuated) subsystem balancing tasks. In this article, we propose a learning-based control design for underactuated balance robots. The key idea integrates a model predictive control method to design the desired internal subsystem trajectory and perform the external subsystem tracking task, while an inverse dynamics controller is used to stabilize the internal subsystem to its desired trajectory. The control design is based on Gaussian process (GP) regression models that are learned from experiments without requiring a priori knowledge about the robot dynamics or the demonstration of successful stabilization. GP regression models also provide estimates of modeling uncertainties of the robotic systems, and these estimations are used to enhance control robustness to modeling errors. The learning-based control design is analyzed with guaranteed stability and performance. The proposed design is demonstrated by experiments on a Furuta pendulum and an autonomous bikebot.

Original languageEnglish (US)
Pages (from-to)572-589
Number of pages18
JournalIEEE Transactions on Robotics
Issue number1
StatePublished - Feb 1 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


  • Balance control
  • Gaussian processes (GPs)
  • model predictive control
  • nonminimum phase systems
  • underactuated robots


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