Fixed-time attitude control based on identifier-actor-critic structure for high-speed morphing vehicles

High-speed Morphing Vehicles (HMVs) embody advanced biomimetic technology but pose significant control challenges due to their fast-varying dynamics, inherent uncertainties, and stringent efficiency requirements. These issues necessitate an advanced attitude control system capable of rapid response, effective disturbance rejection, and green-efficient operation. This paper proposes a dual-loop identifier-actor-critic structure for the attitude control system of HMVs to satisfy these demands. Within this structure, a fixed-time Neural Network (NN)-based identifier provides feedforward compensation to accurately estimate and mitigate system uncertainties in real time. Based on this, a novel fixed-time optimal backstepping control algorithm is developed using actor and critic NNs, which solves the Hamilton-Jacobi-Bellman equation online to achieve optimal performance. All these NNs update their weights online via adaptive laws to enable rapid convergence. Detailed comparative simulations validate the superior effectiveness of the proposed method, confirming the improvements in both tracking performance and energy efficiency for HMV attitude control.