Citation

Huang, Hanyi and Arogbonlo, Adetokunbo and Yu, Samson and Kwek, Lee Chung and Lim, Chee Peng (2025) Trajectory tracking of a SCARA robot using intelligent active force control. Neural Computing and Applications. ISSN 0941-0643

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Abstract

Trajectory tracking with disturbance rejection is a challenging problem in robotics, particularly in applications involving selective compliance articulated robot arms (SCARA). In this paper, we address the trajectory tracking problem with the presence of disturbances in applying SCARA, by designing controllers with active force control (AFC)-based control methods. AFC has shown potential in disturbance rejection, and its per efficiency of the designed controllers, we integrated different machine learning techniques into the AFC controller, including iterative learning (IL), adaptive neuro-fuzzy inference system (ANFIS) and reinforcement learning (RL). Two case studies were conducted and compared with two different benchmark controllers to validate intelligent AFCbased controllers: a port-controlled Hamiltonian (PCH) control and a hybrid proportional-integral-derivative (PID) control. The results demonstrate that the AFC-based controllers consistently outperform the benchmark methods. Specifically, in Case 1, the AFC-RL controller achieves a 99.99% improvement in root mean square error for joint 1 compared to the hybrid PID control. In Case 2, the AFC-RL controller outperforms the AFC-IL controller in trajectory tracking accuracy by 98.71%. Also, disturbance rejection ability was tested on the AFCbased controllers with various types of disturbances. Among the three AFC-based controllers, AFC-RL shows the best performance. The findings highlight the potential of integrating machine learning into AFC for more accurate and efficient robotic control.

Item Type:	Article
Uncontrolled Keywords:	Reinforcement learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	03 Jun 2025 03:51
Last Modified:	03 Jun 2025 03:51
URII:	http://shdl.mmu.edu.my/id/eprint/13920

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