Bayesian Optimization for Automatic Tuning of Torque-Level Nonlinear Model Predictive Control

2025/12/03 by Gabriele Fadini, Fadini, Gabriele, Deepak Ingole +5
Engineering · #Advanced Control Systems Optimization #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Electrical engineering #Iterative Learning Control Systems #Robotic Mechanisms and Dynamics #Robotics (cs.RO) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2512.03772

openalex publication_date 2025/12/03 · openalex created_date 2025/12/05 · openalex updated_date 2026/07/28

Abstract

This paper presents an auto-tuning framework for torque-based Nonlinear Model Predictive Control (nMPC), where the MPC serves as a real-time controller for optimal joint torque commands. The MPC parameters, including cost function weights and low-level controller gains, are optimized using high-dimensional Bayesian Optimization (BO) techniques, specifically Sparse Axis-Aligned Subspace (SAASBO) with a digital twin (DT) to achieve precise end-effector trajectory real-time tracking on an UR10e robot arm. The simulation model allows efficient exploration of the high-dimensional parameter space, and it ensures safe transfer to hardware. Our simulation results demonstrate significant improvements in tracking performance (+41.9%) and reduction in solve times (-2.5%) compared to manually-tuned parameters. Moreover, experimental validation on the real robot follows the trend (with a +25.8% improvement), emphasizing the importance of digital twin-enabled automated parameter optimization for robotic operations.

Citations

• Guided Bayesian Optimization: Data-Efficient Controller Tuning with Digital Twin
• Tuning Legged Locomotion Controllers via Safe Bayesian Optimization
• Bayesian Optimisation for Robust Model Predictive Control under Model Parameter Uncertainty
• High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces
• Scalable Constrained Bayesian Optimization
• Crocoddyl: An Efficient and Versatile Framework for Multi-Contact\n Optimal Control
• Practical Bayesian Optimization of Machine Learning Algorithms

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