A comparative design of a MIMO neural adaptive rate damping for a nonlinear helicopter model

Using a nonlinear 15-state helicopter model in 6 DOF, two di erent neural control systems, both acting as rate damping, have been designed and compared. They are both based on the reference model direct inverse scheme, but they di er each from the other for the identi cation of the inverse model: the rst one is a MIMO feedforward two-layered neural network, while the second one is a combination of three MISO feedforward two-layered neural networks connected in parallel. The strong dynamic cross-coupling, that characterizes the model, has enabled us to verify the actual MIMO capability of both the neural rate damping con gurations. However the multi-MISO version has demonstrated to have a more robust adaptive ability.