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In this paper, the robust adaptive controller is investigated for the longitudinal dynamics of a generic hypersonic flight vehicle. The proposed methodology addresses the issue of controller design and stability analysis with respect to parametric model uncertainty and input saturations for the control-oriented model. The velocity and attitude subsystems are transformed into the linearly parameterized form. Based on the parameter projection estimation, the dynamic inverse control is proposed via the back-stepping scheme. In order to avoid the problem of "explosion of complexity," by introducing a first-order filtering of the synthetic input at each step, the dynamic surface control is designed. The closed-loop system achieves uniform ultimately bounded stability. The compensation design is employed when the input saturations occur. Simulation results show that the proposed approach achieves good tracking performance.

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This paper studies the composite adaptive tracking control for a class of uncertain nonlinear systems in strict-feedback form. Dynamic surface control technique is incorporated into radial-basis-function neural networks (NNs)-based control framework to eliminate the problem of explosion of complexity. To avoid the analytic computation, the command filter is employed to produce the command signals and their derivatives. Different from directly toward the asymptotic tracking, the accuracy of the identified neural models is taken into consideration. The prediction error between system state and serial-parallel estimation model is combined with compensated tracking error to construct the composite laws for NN weights updating. The uniformly ultimate boundedness stability is established using Lyapunov method. Simulation results are presented to demonstrate that the proposed method achieves smoother parameter adaption, better accuracy, and improved performance.

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