The nonlinear modelling and model predictive control of a miniature helicopter UAV

M.Ing.Linear control system theory is well developed and has lead to a number of control system types with well-defined design methods that can be applied to any linear system. Unfortunately, no system in nature is truly linear. As a result, such non-linear systems must be represented by a linear model that is accurate over some region of the operating states of the system. The success of linear control theory in commercial applications is testament to the fact that some types of systems can be adequately represented by a linear model. However, systems with time-varying dynamics or non-linearities such as input or operating state saturation cannot always be adequately controlled by linear control systems. For that reason, non-linear control techniques must be investigated. This project aims to investigate Non-linear Model Predictive Control theory and practical implementation in the context of developing an autopilot for an Unmanned Aerial Vehicle based on a miniature helicopter. A non-linear model of the dynamics of an X-Cell Spectra G radio-controlled helicopter was developed based on the existing literature. A number of experiments were performed to determine the parameters of this model. Significant future work exists in designing additional ground experiments since certain parameters are difficult to measure safely in the laboratory. Additional work to improve the accuracy of the model at high airspeeds, as well as incorporating a more accurate yaw dynamics model, is also required. Following this, a Non-linear Model Predictive Control autopilot was simulated using MATLAB®. The simulation tested the effects of control system parameters such as control horizon and sampling period, as well as the sensor noise susceptibility and its ability to handle wind as a random disturbance. The results determined adequate control system parameters for level flight as well as landing the helicopter under ideal conditions. Simulations in which sensor noise and wind were added showed that the control system is significantly affected by sensor noise and that it cannot hover in the presence of wind. A real-time implementation was not achieved during this work; however, several directions for future research have been discussed