Trajectory control of unmanned aerial vehicle using neural nets with a stable learning algorithm

A neuro-adaptive trajectory control approach for unmanned aerial vehicles is proposed. The aerial robot's altitude and latitude-longitude is controlled by three neuro-adaptive controllers that are used to track the desired altitude, airspeed and roll angle of the vehicle. Each intelligent control module consists of a conventional and a neural network feedback controller. The former is provided both to guarantee global asymptotic stability in compact space and as an inverse reference model of the response of the controlled system. Its output is used as an error signal by a stable on-line learning algorithm to update the parameters of the neurocontroller. In this way the latter is able to eliminate gradually the conventional controller from the control of the system. The proposed learning algorithm makes direct use of the variable structure systems theory and establishes a sliding motion in term of the neurocontroller parameters, leading the learning error toward zero. The performance of the proposed trajectory control scheme is evaluated with time based diagrams under MATLAB's standard configuration and the Aeronautical Simulation Block Set.