Robust Fixed-Time Sliding Mode Attitude Control for a 2-DOF Helicopter Subject to Input Saturation and Prescribed Performance

• Published in: IEEE transactions on transportation electrification Vol. 11; no. 1; pp. 1223 - 1233
• Main Authors: Shen, Hao, Yu, Xin, Yan, Huaicheng, Park, Ju H., Wang, Jing
• Format: Journal Article
• Language: English
• Published: IEEE 01.02.2025
• Subjects: 2-DOF; Artificial neural networks; Attitude control; fixed-time (FT) control; Helicopters; input saturation; Mathematical models; prescribed performance; Propellers; reinforcement learning (RL); sliding mode control; Torque; two-degree-of-freedom helicopters (2-DOF helicopters)
• ISSN: 2332-7782; 2332-7782
• DOI: 10.1109/TTE.2024.3402316

This article explores the issue of robust attitude control for a two-degree-of-freedom helicopters (2-DOF helicopters) system under the fixed-time (FT) control rule. Thanks to the reinforcement-learning strategy, the optimization results for the attitude control objective have been achieved. Under the basic framework of the actor-critic neural networks (A-C NNs), this article not only solves a better solution of the cost-to-go function but also successfully estimates the external disturbance torque existed in the 2-DOF helicopter system. Furthermore, in conjunction with a sliding mode switching mechanism and a novel reaching law, this study introduces a new approach for effectively accomplishing the objective of attitude control while adhering to the constraints of input saturation and prescribed performance. Compared with other types of controllers, a fact can be validated that it has a better action performance of attitude control. In particular, under the action of the controller, each state variable has a stable bound over a specific fixed time. Finally, simulation and comparison examples offer evidence to demonstrate that the proposed control technique is stable.