The present work presents a brief review of some of the notable contributions to piston theory and of its theoretical basis. A generalized formulation of piston theory is given, applicable to both local and classical piston theory. A consistent generalized formulation of the downwash equation is given, accounting for arbitrary motion in the plane of the airfoil. The formulation reduces to established downwash equations through appropriate definition of the cylinder orientation. The theoretical range of validity of classical piston theory is examined, and the relative accuracy of a number of approximate theories encapsulated by the formulation as applied to a planar wedge is considered. The relative importance of higher order terms in piston theory is examined, with the significance of recent literature extending the fidelity of the first-order term highlighted. It is subsequently suggested that current implementations of local piston theory may be improved through the use of a first-order term of suitable accuracy.

The paper presents the development of modelling and control strategies for a six-degree-of-freedom, unmanned combat aerial vehicle with the inclusion of the centre of gravity position travel during the straight-leg part of an in-flight refuelling manoeuvre. The centre of gravity position travel is found to have a parabolic variation with an increasing mass of aircraft. A nonlinear dynamic inversion-based neurocontroller is designed for the process under investigation. Three radial basis function neural networks are exploited in order to invert the dynamics of the system, one for each control channel. Modal and time-domain analysis results show that the dynamic properties of the aircraft are strongly influenced during aerial refuelling. The effectiveness of the proposed control law is demonstrated through the use of simulation results for an F-16 aircraft. The longitudinal neurocontroller provided interesting results, and performed better than a baseline nonlinear dynamic inversion controller without neural network. On the other hand, the lateral-directional nonlinear dynamic inversion-based neurocontroller did not perform well as the longitudinal controller. It was concluded that the nonlinear dynamic inversion-based neurocontroller could be applied to control an unmanned combat aerial vehicle during aerial refuelling.