Automatic Control of an Asymmetric Fighter Aircraft Performing Supermanoeuvres
Keywords:centre-of-gravity, neural network, sliding mode, supermanoeuvre
Center-of-gravity (c.g.) of a combat aircraft may deviate significantly from the plane of symmetry due to asymmetric release of stores, leading to a highly coupled asymmetric six degree-of-freedom (6-DOF) dynamics. Additional nonlinearity and cross-coupling between the longitudinal and lateral-directional dynamics result when the aircraft attempts some supermanoeuvres under such asymmetric conditions. This renders nonlinear control implementation almost unavoidable for the safety of the aircraft. However, success of such control schemes heavily depends on the accurate onboard information of the actual asymmetric c.g. location. The present paper proposes a novel neural network aided sliding mode based hybrid control scheme which does not require such online c.g. information at all. The neural controller part is trained offline so that it can compensate for the deviations in the aircraft dynamics arising from the lateral mass asymmetry and the sliding controller is designed assuming the nominal or symmetrical dynamics to execute the intended manoeuvres. To validate the usefulness of the proposed control scheme, two well-known supermanoeuvres cobra and Herbst are simulated and it is shown that the manoeuvre performance does not get affected appreciably even under a wide range of lateral c.g. movements.
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