The design of online adaptive neural networks for use in a nonlinear helicopter e ight control architecture is treated. Emphasis is given to network architecture and the effect that varying the adaptation gain has on performance. Conclusionsarebasedon asix-degree-of-freedom nonlinearevaluation model ofan attackhelicopter and ametricthatmeasuresthenetwork’ sability tocancel theeffectofmodeling errorsforacomplicated maneuver. Thenetwork isshownto providenearlyperfecttracking in thefaceofsignie cantmodelingerrorsand,additionally, to cancel the model inversion error after a short initial period of learning. Furthermore, it is shown that the performance varies gracefully and monotonically improves as the adaptation gain parameter is increased. The effect on control effort is modest and is mainly perceptible only during a short training episode that can be associated with transition from hover to forward e ight.

Analysis of Adaptive Neural Networks for Helicopter Flight Control

This paper describes a hybrid approach to the problem of controlling a class of nonlinear systems in the face of both unknown nonlinearities and unmodeled dynamics. In the proposed methodology, neural networks and direct adaptive control compensate for unknown nonlinear mappings while dynamic nonlinear damping provides robustness to unmodeled dynamics. To illustrate the theoretical development, the authors design a longitudinal autopilot for a simplified nonlinear missile aerodynamic model with input unmodeled dynamics. Simulation results demonstrate robustness in the face of this type of uncertainty.

Robust adaptive control of uncertain nonlinear systems using neural networks

Trigonometric RBF neural robust controller design for a class of nonlinear system with linear input unmodeled dynamics

Previous research has shown that neural networks can be used to improve upon approximate dynamic inversion controllers in the case of uncertain nonlinear systems. In the one possible architecture, the neural network adaptively cancels linearization errors through on-line learning. Learning may be accomplished by a simple weight update rule derived from Liapunov theory, thus assuring the stability of the closed-loop system. In this paper, the authors apply this methodology to design a bank-to-turn autopilot for an agile anti-air missile. First, a control scheme based on approximate inversion of the vehicle dynamics is presented. This nonlinear control system is then augmented by the addition of a feedforward neural network with on-line learning. Finally, the resulting control law is demonstrated in a nonlinear simulation, and its performance is evaluated relative to a more traditional gain-scheduled linear autopilot. (Author)

Neural-adaptive nonlinear autopilot design for an agile anti-air missile

Neural networks for stable adaptive control of air-to-air missiles

Helicopter nonlinear control using adaptive feedback linearization

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Helicopter nonlinear control using adaptive feedback linearization