Comparing Data-Driven motion tracking controllers for a Flexible-Joint Robotic Manipulator
- Resource Type
- Conference
- Authors
- Espinosa, Daniel; Pacheco, Sergio; Tejada, Juan C.; Manrique, Tatiana
- Source
- 2021 IEEE 5th Colombian Conference on Automatic Control (CCAC) Automatic Control (CCAC), 2021 IEEE 5th Colombian Conference on. :280-285 Oct, 2021
- Subject
- Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Adaptation models
Tracking
Dynamics
Collaboration
Switches
Machine learning
Kinematics
Flexible-joint robot
Switched systems
System identification
Data-driven controller
Neural PID
- Language
Modeling and control of flexible robotic manipulators in collaborative robotics applications, face key issues when it comes to properly including non-linearities but keeping motion models and controllers easy to handle. Machine learning (ML) strategies stand as well suited solutions to obtain simplified models and derive controllers for flexible-joints or flexible-links manipulators. In the present paper data-driven dynamics analysis and controller design for a Flexible-Joint Robotic Manipulator (FJRM) are presented. The FJRM under study is a planar two-DOF manipulator with two flexible-joints and two rigid-links with a switched dynamics. The implementation hereby described is determined by a comparative analysis developed between direct and indirect data-driven controllers. Firstly, state-space feedback is proposed from an experimentally identified model as an indirect framework. Secondly, a Neural PID is designed and developed directly from data. The comparison results allowed to identify the most appropriate controller topology to implement.