TUHH Open Research
 

Combined feedforward and feedback dynamic trajectory tracking control of a soft robot

Issue
2026
Start Page
1111
End Page
1116
Citation
European Control Conference, ECC 2026
Contribution to Conference
European Control Conference, ECC 2026  
Publisher Link
https://ieeexplore.ieee.org/abstract/document/11625246
Scopus ID
2-s2.0-105047324395
Publisher
IEEE
ISBN of container
979-8-3315-5755-3
978-3-907144-13-8
Soft robots are an emerging and fast-growing field of research with applications in various areas. With the rise of new soft robotic applications, control requirements increase. Thus, control of dynamic motion is becoming more and more important. The main challenges in controlling soft robots stem from their large, continuous deformations, which lead to many degrees of freedom, as well as the difficulty of sensor integration, which leads to a limited availability of control feedback. Additionally, unknown material properties and comparatively large manufacturing inaccuracies hinder reliable modeling of soft robots. So far, in soft robotics mainly so-called quasi-static control approaches, which neglect the dynamics of the system, are used. However, modern soft robotic applications demand increasingly faster and more accurate movements. In these cases, quasi-static controllers are insufficient, and controllers which account for the robots dynamics are needed.In this work different feedforward and feedback control approaches for dynamic trajectory tracking control of soft robots are implemented and examined. A neural-network-based quasi-static feedforward controller, a dynamic feedforward controller based on the inversion of a dynamic piecewise constant curvature (PCC) model using servo-constraints, and the combination of the two different feedforward controllers with a linear quadratic regulator with integral action (LQRi) and gain scheduling are considered. For feedback control, only sensor feedback obtained from a curvature sensor integrated into the soft robot is used. The performance of these approaches is compared in simulations and experiments.
Subjects
Modeling
Soft robotics
Arrays
Printing
Trajectory
Trajectory tracking
Image sensors
Tendons
Equations
Feedback control
DDC Class
627: Hydraulic Engineering
629.892: Robot