On Koopman-based surrogate models for non-holonomic robots

2023/03/16 by Lea Bold, Hannes Eschmann, Bold, Lea +7 · 2 citations
Engineering · Physics and Astronomy · #37Mxx #68T40 #93C15 #93C57 #93C85 #FOS: Computer and information sciences #FOS: Mathematics #Hydraulic and Pneumatic Systems #Model Reduction and Neural Networks #Optimization and Control (math.OC) #Real-time simulation and control systems #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2303.09144

openalex publication_date 2023/03/16 · openalex created_date 2023/03/19 · openalex updated_date 2026/08/01

Abstract

Data-driven surrogate models of dynamical systems based on the extended dynamic mode decomposition are nowadays well-established and widespread in applications. Further, for non-holonomic systems exhibiting a multiplicative coupling between states and controls, the usage of bi-linear surrogate models has proven beneficial. However, an in-depth analysis of the approximation quality and its dependence on different hyperparameters based on both simulation and experimental data is still missing. We investigate a differential-drive mobile robot to close this gap and provide first guidelines on the systematic design of data-efficient surrogate models.

Cited by

• Excitation of control-affine systems and Koopman error bounds
• Two-component controller design to safeguard data-driven predictive control

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