Sep 2026· International Conference on Mechatronics and Electronic Technology· Vol 14358, pp. 143580F - 143580F-8· 0 citations· 15 references
Engineering
TL;DR
An optimal trajectory-tracking control method is developed for a two-degree-of-freedom (2-DOF) helicopter operating under uncertain dynamics and external disturbances, and an actor–critic learning structure is embedded into the controller design.
Abstract
An optimal trajectory-tracking control method is developed for a two-degree-of-freedom (2-DOF) helicopter operating under uncertain dynamics and external disturbances. To cope with the strong nonlinear behavior of the system and improve tracking performance, a reinforcement-learning-assisted backstepping control framework is constructed. The helicopter dynamics are first reformulated into a state-space representation, where neuralnetwork identifiers are introduced to approximate the unknown dynamics. Furthermore, an actor–critic learning structure is embedded into the controller design: the critic approximates the performance index associated with the current policy, while the actor updates the control input toward optimality. After theoretic analysis, the boundedness of 2-DOF helicopter system is rigorously established, and the tracking errors are guaranteed to be ultimately confined within a sufficiently small compact around zero. Numerical studies are carried out to validate the applicability and tracking capability of the developed method.
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