Adaptive learning-based optimal tracking control system design and analysis of a disturbed nonlinear hypersonic vehicle model

Abstract

We propose an adaptive learning-based optimal control scheme for height-velocity control models considering model uncertainties and external disturbances of hypersonic winged-cone vehicles. The longitudinal nonlinear model is first established and transformed into the control-oriented error equations, and the control scheme is organized by a steady-compensation combination. To overcome and eliminate the impact of model uncertainties and external disturbances, an adaptive radial basis function neural network (RBFNN) is designed by a q-gradient approach. Taking the height-velocity error system with estimated uncertainties into account, the adaptive learning-based optimal tracking control (ALOTC) scheme is proposed by combining the critic-only adaptive dynamic programming (ADP) framework and parameter optimization of system settling time. Furthermore, a novel weight update law is proposed to satisfy the online iteration requirements, and the algorithm convergence and closed-loop stability are discussed by the Lyapunov theory. Finally, four simulation cases are provided to prove the effectiveness, accuracy, and robustness of the proposed scheme for the hypersonic longitudinal control system.