Browsing by Author "Panday, Aarti"

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    Indirect adaptive control of an unmanned receiver and tanker system during aerial refuelling
    (2024) Panday, Aarti
    Unmanned aerial vehicles (UAVs) have evolved in design, complexity and have seen increasingly diverse roles within the past five decades of its adoption as an essential aviation tool. The lack of an in-flight-refuelling capability is one of the major practical limitations of UAVs. In a successful automated aerial refuelling (AAR) manoeuvre, the refuelling receiver aircraft must maintain an accurate position relative to the tanker aircraft. Without the presence of a skilled pilot in the cockpit, control systems must be developed to meet the stringent performance and safety requirements of the AAR manoeuvre, while requiring stability and performance robustness. The aim of this study was to address the development of stable control laws by synthesising techniques in nonlinear and intelligent control. The objectives included system model development, optimisation of controller designs using evolutionary algorithms coupled with Lyapunov stability theory, and system simulation both with and without disturbances. The research objectives were addressed in three phases using numerical simulation in the MATLAB environment: establishing a baseline case in phase 1 where proportional-integral-derivative (PID) control was used, followed by the development of a Lyapunov-based feedback linearisation controller in phase 2 and lastly, the development of a dynamic neural network-based control scheme in phase 3. An aerial refuelling system model was developed, inclusive of the tanker-receiver engagement, where the receiver aircraft dynamics was framed as a variable-mass system subject to a wind field as inherent to the process. Three controllers were designed for the system. The nonlinear system model was used directly in the development of a benchmark controller case using PID control for the aerial refuelling manoeuvre. A single set of controller gains, applicable throughout all phases of the refuelling manoeuvre, was as found through manual tuning, eliminating the need for gain scheduling. Lyapunov stability theory was extended to the aerial refuelling system, where mechanical energy of the variable-mass system was increased as fuel was added to the receiver aircraft. Stability was established for the system throughout refuelling, including the post-refuelling phase, where the receiver aircraft would remain on station and mechanical energy would be conserved. The stability theorem was incorporated into a cost function, whose minimisation using evolutionary algorithms, would lead to optimisation of controller gains that would ensure system iv stability. The manually-tuned controller gains for the benchmark PID controller were optimised using particle swarm optimisation and genetic algorithms (GAs). It was found through the minimisation routine, that there is a minimum energy threshold that must not be exceeded in the system for performance and stability to be maintained. The minimum energy was found to be related to the ratio of mass increase to total aircraft mass. Two disturbance cases were defined: decreased, asymmetric refuelling and turbulence, to test the robustness of the designed controller. The second controller designed was based on feedback linearisation (FBL). FBL was incorporated in the inner loop, while a pseudo-backstepping scheme in the command inversion loop converted the reference command signals into desired dynamics. PID tracking control was used to close the outer loop to give effect to controller tracking objectives. Controller gains were selected through manual tuning and were then optimised with the Lyapunov-based GA routine developed earlier. The third controller design was the synthesised nonlinear intelligent controller, which used dynamic neural networks (DNNs) in an indirect adaptive FBL control scheme in the inner loop, while the outer loop remained the same as the FBL controller without the DNN. System identification was performed in order to obtain the DNN models to estimate the plant dynamics. Controller gains were selected through manual tuning and were then optimised with the Lyapunov-based GA routine developed earlier. Dynamic simulation results showed that the system was sensitive in the lateral-directional states in the presence of disturbances. The benchmark PID control was the least robust to disturbances, but its performance was improved when the Lyapunov-based GA gains were applied. The manually-tuned FBL controller exhibited damped oscillatory behaviour in the lateral-directional states during refuelling, however, application of the Lyapunov-based GA gains led to these behaviours being eradicated from the system state-variable trajectories. The DNN-based FBL controller with Lyapunov-based GA-optimised gains performed the best from all designed controllers. Transient behaviour with maximum deviation of 0.004% from nominal in the longitudinal and vertical separation was observed in the DNN-based FBL controller prior to refuelling commencing, but this very quickly settled to nominal. Application of the Lyapunov-based optimisation gave improved controller performance compared to the manually-tuned control gains in all instances, and the system was able to return to its nominal or desired values when subjected to disturbances. For the AAR manoeuvre, the synthesis of nonlinear and intelligent control techniques was able to produce stable control which showed good robustness characteristics when subjected to disturbances.
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    NDI-based neurocontroller for unmanned combat aerial vehicles during aerial refuelling.
    (2010-02-05T11:31:14Z) Panday, Aarti
    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.