Intelligent compensation prescribed performance nonlinear dynamic inverse control for air-breathing hypersonic vehicle

This study addresses the guidance and control challenges of air-breathing hypersonic vehicles (AHVs) under vehicle/engine integrated coupling conditions by proposing an intelligent compensation-based prescribed performance nonlinear dynamic inverse control method. Firstly, based on the coupling dynamics equation of air-breathing hypersonic vehicle and the prescribed performance control theory, the movement and attitude prescribed performance control models are established, and nonlinear dynamic inverse reference control commands are designed. Subsequently, the adaptively adjustable performance function is developed, with its stability under adjustment conditions rigorously proven, thereby enhancing the function’s envelope capability in complex command tracking scenarios. Notably, to mitigate the impact of acceleration coefficient identification errors and unknown disturbances on trajectory tracking control, the radial basis function (RBF) neural network is employed to online estimate the total disturbance, complemented by an adaptive law for network weight parameters to bolster the control method’s disturbance rejection capabilities. Mathematical simulations demonstrate that, under ±20% parameter uncertainties, the proposed method ensures convergence within the performance envelope, reducing the maximum terminal error by over 95% compared to the linearized coupling control method. Furthermore, it effectively avoids singular values in control command solutions under multiple step command conditions, validating the efficacy of the adaptive performance function adjustment mechanism.