Multi-Segment Soft Robot Control Via Deep Koopman-Based Model Predictive Control

Soft robots, compared to regular rigid robots, as their multiple segments with soft materials bring flexibility and compliance, have the advantages of safe interaction and dexterous operation in the environment. However, due to its characteristics of high dimensional, nonlinearity, time-varying nature, and infinite degree of freedom, it has been challenges in achieving precise and dynamic control such as trajectory tracking and position reaching. To address these challenges, we propose a framework of Deep Koopman-based Model Predictive Control (DK-MPC) for handling multi-segment soft robots. We first employ a deep learning approach with sampling data to approximate the Koopman operator, which therefore linearizes the high-dimensional nonlinear dynamics of the soft robots into a finite-dimensional linear representation. Secondly, this linearized model is utilized within a model predictive control framework to compute optimal control inputs that minimize the tracking error between the desired and actual state trajectories. The real-world experiments on the soft robot “Chordata” demonstrate that DK-MPC could achieve highprecision control, showing the potential of DK-MPC for future applications to soft robots. More visualization results can be found at https://pinkmoon-io.github.io/DKMPC/.

Authors

• Lei Lv
• Lei Liu 0076
• Lei Bao
• Fuchun Sun 0001
• Jiahong Dong
• Jianwei Zhang 0001
• Xuemei Shan
• Kai Sun
• Hao Huang 0009
• Yu Luo

Keywords

• Deep learning
• Uncertainty
• Trajectory tracking
• Optimal control
• Data visualization
• Soft robotics
• Predictive models
• Nonlinear dynamical systems
• Trajectory
• Predictive control
• Model Predictive Control
• Soft Robots
• Control Of Soft Robots
• Linear Model
• High-dimensional
• Precise Control
• Control Input
• Nonlinear Dynamics
• Soft Materials
• Tracking Error
• Real-world Experiments
• Chordates
• Linear Representation
• Robot Dynamics
• Optimal Control Input
• Rigid Robots
• Model Predictive Control Framework
• Deep Neural Network
• Actuator
• Traditional Control Methods
• Nonlinear Systems
• Positive Semidefinite Matrix
• Latent Space
• Vision Sensors
• Linear Dynamics
• Linear Operator
• Soft Robotic Applications
• Model-based Control
• Reference Trajectory