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.

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.

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This paper investigates a fault-tolerant control of the hypersonic flight vehicle using back-stepping and composite learning. With consideration of angle of attack (AOA) constraint caused by scramjet, the control laws are designed based on barrier Lyapunov function. To deal with the unknown actuator faults, a robust adaptive allocation law is proposed to provide the compensation. Meanwhile, to obtain good system uncertainty approximation, the composite learning is proposed for the update of neural weights by constructing the serial-parallel estimation model to obtain the prediction error which can dynamically indicate how the intelligent approximation is working. Simulation results show that the controller obtains good system tracking performance in the presence of AOA constraint and actuator faults.

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