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This paper explores the issue of robust attitude control for a 2-DOF helicopter system under the fixed-time 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, this paper 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.
Shen et al. (Fri,) studied this question.