Fuzzy TSMCSPO for Trajectory Tracking of Nuclear Reactor Dismantlement Robot Manipulator
Chengqian Li, Hamza Khan, Jinwon Lee, Jae‐Hyung Kim, Min Cheol Lee
- Year
- 2023
- Citations
- 7
- Access
- Open access
Abstract
In this study, a fuzzy logic system to tune the parameters of terminal sliding mode control with a sliding perturbation observer (TSMCSPO) in real-time is proposed according to the system’s state changes to improve the control performance of TSMCSPO for a 5-DOF robot manipulator used for nuclear reactor dismantlement. Accurate trajectory tracking control is required when using a multi-DOF robot manipulator to cut the decommissioned nuclear reactor. A TSMCSPO scheme using TSMC in SPO has been proposed in previous research, which improves the estimation and convergence performance of traditional SMCSPO. In TSMCSPO, the parameters of the controller are important to enhance the control performance. Moreover, due to the influence of the working environment and the uncertainty of the system, the characteristics of the system will be changed during the trajectory tracking control of the multi-degree-of-freedom robot manipulator. Therefore, real-time optimal parameter tuning is required. In this regard, using fuzzy logic to tune the parameters is an effective method. By designing fuzzy rules, the controller parameters can be optimized. In this study, the proposed algorithm and, the trajectory tracking control simulation of the robot manipulator in the nuclear reactor vessel internal (RVI) are implemented in MATLAB/Simulink environment. The control algorithm is verified by the experiments on a real robot manipulator platform. The outstanding control performance of the algorithm is demonstrated by comparing the trajectory tracking error between the proposed fuzzy TSMCSPO scheme and TSMCSPO with fixed parameters.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002