This work presents a nonlinear adaptive dynamic surface air speed and a flight path angle control design procedure for the longitudinal dynamics of a generic hypersonic flight vehicle. The proposed design scheme takes into account the magnitude, rate, and bandwidth constraints on the actuator signals. A new approach is used to enhance tracking performance and avoid a large initial control signal. The uncertain nonlinear functions in the flight vehicle model are approximated by using radial basis function neural networks. A detailed stability analysis of the designed controllers shows that all the signals of the closed‐loop system are uniformly ultimately bounded. The robust performance of the design scheme is verified through numerical simulations of the flight vehicle model for various parameter variation test cases. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

No abstract

No abstract

An adaptive dynamic surface control scheme for actuator failures compensation in a class of nonlinear system is presented. Radial basis function neural networks (RBF NNs) are incorporated into our controller design, for approximating the nonlinearities around the known nominal model. The RBF NNs compensate the system dynamics uncertainties and disturbance induced by actuator failures. The closed-loop signals of the system are proven to be uniformly ultimately bounded (UUB) by Lyapunov analysis. The output tracking error is bounded within a residual set which can be made small by appropriately choosing the controller parameters. We show the effectiveness of our approach by simulating the longitudinal dynamics of a twin otter aircraft with half portion of the elevator failing at unknown value and time instant.

The design of a nonlinear adaptive dynamic surface controller for the longitudinal model of a hypothetical supersonic flight vehicle is considered in this work. The uncertain nonlinear functions in the strict feedback flight vehicle model are approximated by using radial basis function neural networks. A detailed stability analysis of the designed angle-of-attack controller shows that all the signals of the closed loop system are uniformly ultimately bounded. The performance of the designed controller is verified through numerical simulations of the flight vehicle model.