NDI-based neurocontroller for unmanned combat aerial vehicles during aerial refuelling.

The success of Unmanned Combat Aerial Vehicles (UCAVs) requires further developments in the field of automated aerial refuelling (AAR) and control systems. AAR aircraft models identified thus far do not take the centre of gravity (cg) position movement into account during refuelling. A six-degree-of-freedom aircraft model was combined with a moving cg model for refuelling. The equations of motion for the aircraft in flight refuelling showed the aircraft dynamics to be coupled in the longitudinal and lateral-directional planes when the cg had moved away from the reference point. Applying assumptions specific to the flight conditions, simplified equations of motion were derived. Modal analysis of four cases for the linearised aircraft model during aerial refuelling was conducted. This revealed that the increase in mass was favourable to the stability of the Dutch Roll mode, but the mode did become more oscillatory initially as mass was increased, but as the cg moved forward, the mode became less oscillatory. The opposite was observed with the Phugoid mode. The Short Period Oscillation (SPO) decomposed into two first order modes during refuelling and these remained unchanged during the refuelling process. Three radial basis function (RBF) neural networks (RBFNN) were developed and trained to approximate the inverse plant dynamics and predicted commanded deflections of the elevator, aileron and rudder. Training data required for the network was randomly generated and the desired rates and commanded control surface deflections were computed. The training error was the smallest in the elevator deflection required during refuelling. A basic nonlinear dynamic inversion (NDI) controller without a neural network (NN) was designed for the aircraft. The performance of this controller was not satisfactory. The RBF was combined with the NDI to form a RBFNN-based controller. The longitudinal NDI RBFNN-based controller was less sensitive to modelling errors than the base NDI controller. The lateral NDI RBFNN-based controller’s performance was worse than the longitudinal controller, but showed potential as a technique for future consideration. Including the variation of aircraft inertia in the model has been recommended as further work, as well as exploring other neural network topologies in the NDI NN controller

Generalized Dynamic Inversion Based Aircraft LateraL Control

This paper illustrates how the Generalized Dynamic inversion (GDI) is used to control aircraft lateral motion. To implement the GDI control law, the yaw channel constraint dynamics are first constructed and then inverted using Moore-Penrose Generalized Inverse (MPGI). Consequently, the auxiliary component of this control law is affine in a null control vector, which is designed to guarantee asymptotic aircraft stability. Asignificant benefit of GDI  control law is the additional design flexibility afforded by its two independent control actions. Extensive simulations have been conducted to prove the efficacy of the proposed method

A Unified Approach to Nonlinear Dynamic Inversion Control with Parameter Determination by Eigenvalue Assignment

This paper presents a unified approach to nonlinear dynamic inversion control algorithm with the parameters for desired dynamics determined by using an eigenvalue assignment method, which may be applied in a very straightforward and convenient way. By using this method, it is not necessary to transform the nonlinear equations into linear equations by feedback linearization before beginning control designs. The applications of this method are not limited to affine nonlinear control systems or limited to minimum phase problems if the eigenvalues of error dynamics are carefully assigned so that the desired dynamics is stable. The control design by using this method is shown to be robust to modeling uncertainties. To validate the theory, the design of a UAV control system is presented as an example. Numerical simulations show the performance of the design to be quite remarkable

A NONLINEAR DYNAMIC INVERSION–BASED NEUROCONTROLLER FOR UNMANNED COMBAT AERIAL VEHICLES DURING AERIAL REFUELLING

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