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.

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.

This paper studies both indirect and direct global neural control of strict-feedback systems in the presence of unknown dynamics, using the dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive neural controller in the neural approximation domain, together with the robust controller that pulls the transient states back into the neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global neural DSC design.

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With the capability of high speed flying, a more reliable and cost efficient way to access space is provided by hypersonic flight vehicles. Controller design, as key technology to make hypersonic flight feasible and efficient, has numerous challenges stemming from large flight envelope with extreme range of operation conditions, strong interactions between elastic airframe, the propulsion system and the structural dynamics. This paper briefly presents several commonly studied hypersonic flight dynamics such as winged-cone model, truth model, curve-fitted model, control oriented model and re-entry motion. In view of different schemes such as linearizing at the trim state, input-output linearization, characteristic modeling, and back-stepping, the recent research on hypersonic flight control is reviewed and the comparison is presented. To show the challenges for hypersonic flight control, some specific characteristics of hypersonic flight are discussed and the potential future research is addressed with dealing with actuator dynamics, aerodynamic/reaction-jet control, flexible effects, non-minimum phase problem and dynamics interaction.