Browsing by Author "Kantue, Paulin"

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    Integrated fault-tolerant control system for unmanned aerial systems
    (2024) Kantue, Paulin
    The susceptibility of unmanned aerial vehicles (UAVs) to faults and errors within critical functions such as flight control and navigation systems, combined with their inability in supporting mechanical redundancy due to their size and weight constraints, has led to the research and development of intelligent and fault accommodating control systems known as fault-tolerant control systems (FTCS). The main objective of this research is to design a faulttolerant control (FTC) system which makes use of only flight data measurements available in most UAVs, to augment mission integrity against actuator faults. This thesis presents new research into the field of UAV fault-tolerant control. The above-stated data-driven approach in FTC design consisted of using radial basis functions neural networks (RBFNN), combined with a technique of time difference of arrival (TDOA) to detect and identify a particular type of actuator fault called an incipient fault. System identification of a propeller-motor slippage condition enabled the model estimation of such an incipient behaviour. FTC integration issues such as: FTC reliability and implementation in a real-time operating system; fault detection and diagnosis (FDD), and controller reconfiguration delays, were investigated within a development framework which ensured online fault estimation. This was achieved by adopting a modified RBFNN training algorithm with fast convergence and low-memory capabilities. The framework also incorporated a controller reconfiguration mechanism using the extremum seeking control law combined with an optimisation function constructed by utilising a geometric representation for actuator allocation. The integrated FTC requirement to improve the real-time performance of an unmanned quadcopter under various levels of incipient fault was achieved by comparing with a nominal controller within real-time simulation environment. The major contributions of this research can be summarised as follows: (1) The development of a fault-emulation model based on the faulty behaviour of a propeller-motor slippage (incipient) condition validated using a software-in-the-loop (SITL) simulation environment; (2) The development of a TDOA framework and the real-time learning of RBFNN through a meta-heuristic hybrid line search algorithm for real-time FDD. (3) The development and real-time testing of an extremum seeking reconfiguration control algorithm to improve the probability of mission success implemented within an integrated fault-tolerant framework
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    Online parameter estimation of a miniature unmanned helicopter using neural network techniques
    (2012-02-01) Kantue, Paulin
    The online aerodynamic parameter estimation of a miniature unmanned helicopter using Neural Network techniques has been presented. The simulation model for the miniature helicopter was developed using the MATLAB/ SIMULINK software tool. Three trim conditions were analyzed: hover flight, 10m/s forward flight and 20m/s forward flight. Radial Basis Function (RBF) online learning was achieved using a moving window algorithm which generated an input-output data set at each time step. RBF network online identification was achieved with good robustness to noise for all flight conditions. However, the presence of atmospheric turbulence and sensor noise had an adverse effect on network size and memory usage. The Delta Method (DM) and the Modified Delta Method (MDM) was investigated for the NN-based online estimation of aerodynamic parameters. An increasing number high confidence estimated parameters could be extracted using the MDM as the helicopter transitioned from hover to forward flight.