Universal approximation using radial-basis-function networks
Neural Computation
A direct adaptive neural command controller design for an unstable helicopter
Engineering Applications of Artificial Intelligence
Adaptive output feedback control of nonlinear systems using neural networks
Automatica (Journal of IFAC)
Brief A combined backstepping and small-gain approach to adaptive output feedback control
Automatica (Journal of IFAC)
Nonlinear modelling and control of helicopters
Automatica (Journal of IFAC)
Adaptive control using neural networks and approximate models
IEEE Transactions on Neural Networks
Output feedback control of nonlinear systems using RBF neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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This paper presents an on-line learning adaptive neural control scheme for helicopters performing highly nonlinear maneuvers. The online learning adaptive neural controller compensates the nonlinearities in the system and uncertainties in the modeling of the dynamics to provide the desired performance. The control strategy uses a neural controller aiding an existing conventional controller. The neural controller is based on a online learning dynamic radial basis function network, which uses a Lyapunov based on-line parameter update rule integrated with a neuron growth and pruning criteria. The online learning dynamic radial basis function network does not require a priori training and also it develops a compact network for implementation. The proposed adaptive law provides necessary global stability and better tracking performance. Simulation studies have been carried-out using a nonlinear (desktop) simulation model similar to that of a BO105 helicopter. The performances of the proposed adaptive controller clearly shows that it is very effective when the helicopter is performing highly nonlinear maneuvers. Finally, the robustness of the controller has been evaluated using the attitude quickness parameters (handling quality index) at different speed and flight conditions. The results indicate that the proposed online learning neural controller adapts faster and provides the necessary tracking performance for the helicopter executing highly nonlinear maneuvers.