Advanced Control Strategies and System Integration for 2DOF Robotic Manipulator
Zhao, Ruixue (2025-07-02)
Advanced Control Strategies and System Integration for 2DOF Robotic Manipulator
(02.07.2025)
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
suljettu
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2025073080238
https://urn.fi/URN:NBN:fi-fe2025073080238
Tiivistelmä
This thesis explores advanced control strategies and system integration for a two-degree-of-freedom (2DOF) robotic manipulator. The purpose of the research is to develop and evaluate an intelligent control approach that improves trajectory tracking performance compared to conventional methods. The methodology follows a quantitative approach, beginning with the theoretical modelling of the manipulator’s kinematics and dynamics, and the design of an intelligent controller based on a feedback linearization (FBL) approach as the main control framework, with a radial basis function (RBF) neural network embedded to compensate for system uncertainties, nonlinearities, and external disturbances. The system is first tested in a simulation environment, and then extended to a virtual and a physical robotic manipulator (Quanser QArm) through trial experiments. A custom user control interface is also integrated to enable control and real-time monitoring.
Research data are collected from simulations, virtual and physical robotic manipulator connection tests by tuning control parameters and observing outputs such as joint trajectories, control signals, tracking errors, and disturbance estimates. These results are used to compare the intelligent controller with a conventional FBL controller. The findings show that the intelligent controller offers improved tracking accuracy and robustness to disturbances. Additionally, noticeable differences are observed in controller behaviour across simulation, virtual, and real environments.
The research concludes with the successful implementation of intelligent control and system integration for the 2DOF robotic manipulator, and demonstrates that the proposed intelligent controller performs better than the conventional FBL controller. This work provides a foundation for future extensions to 3DOF robotic manipulator and integration with control algorithms tailored to specific tasks, such as visual servoing or colour-based object sorting.
Research data are collected from simulations, virtual and physical robotic manipulator connection tests by tuning control parameters and observing outputs such as joint trajectories, control signals, tracking errors, and disturbance estimates. These results are used to compare the intelligent controller with a conventional FBL controller. The findings show that the intelligent controller offers improved tracking accuracy and robustness to disturbances. Additionally, noticeable differences are observed in controller behaviour across simulation, virtual, and real environments.
The research concludes with the successful implementation of intelligent control and system integration for the 2DOF robotic manipulator, and demonstrates that the proposed intelligent controller performs better than the conventional FBL controller. This work provides a foundation for future extensions to 3DOF robotic manipulator and integration with control algorithms tailored to specific tasks, such as visual servoing or colour-based object sorting.