2021/03/01 by Lu Shi, Shi, Lu, Konstantinos Karydis +1
Decision Sciences · Engineering · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Electrical engineering #Fluid Dynamics and Turbulent Flows #Model Reduction and Neural Networks #Probabilistic and Robust Engineering Design #Robotics (cs.RO) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2103.00812
openalex publication_date 2021/03/01 · openalex created_date 2021/03/15 · openalex updated_date 2026/07/28
Koopman operator theory has served as the basis to extract dynamics for nonlinear system modeling and control across settings, including non-holonomic mobile robot control. There is a growing interest in research to derive robustness (and/or safety) guarantees for systems the dynamics of which are extracted via the Koopman operator. In this paper, we propose a way to quantify the prediction error because of noisy measurements when the Koopman operator is approximated via Extended Dynamic Mode Decomposition. We further develop an enhanced robot control strategy to endow robustness to a class of data-driven (robotic) systems that rely on Koopman operator theory, and we show how part of the strategy can happen offline in an effort to make our algorithm capable of real-time implementation. We perform a parametric study to evaluate the (theoretical) performance of the algorithm using a Van der Pol oscillator and conduct a series of simulated experiments in Gazebo using a non-holonomic wheeled robot.