As the development of highways, it is quite normal for buses running in a speed around 100km/h. When buses are running in a high speed, they may suffer from the influence of side wind disturbances at anytime. Sometimes, it may result in traffic accidents. Therefore, the study of bus stability under side wind disturbances becomes more and more important. Due to restrictions of real tests, computer simulation can be used to study this subject. The bus side wind response character is reflected through the driver’s manoeuvre , so open-loop analysis is hard to give a comprehensive evaluation of the side wind stability of the bus. Therefore, closed-loop analysis is studied in this thesis. An ADAMS bus model and a side wind force model are developed in this thesis, along with two driver models, the PID control model and the preview curvature model. The driver models are built in Simulink and co-simulation between ADAMS/View and Simulink is conducted. The results of co-simulation show that the two driver models can both control the bus from deviating from the desired course under side wind disturbances. The PID control model is simple and shows a very good control effect. The maximum lateral displacement of the bus by PID control model is just 0.0205m under maximum side wind load 1000N and 2500Nm when preview time is 1.2s, while it is 0.0702m by preview curvature model, however, it is difficult to determine the coefficients Kd, Kp, and Ki in the PID controller. The preview curvature model also shows a good control effect in terms of the maximum lateral displacement and yaw angle of the bus. Comparing these two models, the PID control model is more sensitive to deviations, with quicker response and larger steering input. The bus model system is stable under side wind disturbances. Through driver ’s proper steering manoeuvre, the bus is well controlled. The closed-loop analysis is a good method to study the bus stability under side wind disturbances.

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Quad-rotor helicopters are agile aircraft which are lifted and propelled by four rotors. Unlike traditional helicopters, they do not require a tail-rotor to control yaw, but can use four smaller fixed-pitch rotors. However, without an intelligent control system it is very difficult for a human to successfully fly and manoeuvre such a vehicle. Thus, most of recent research has focused on small unmanned aerial vehicles, such that advanced embedded control systems could be developed to control these aircrafts. Vehicles of this nature are very useful when it comes to situations that require unmanned operations, for instance performing tasks in dangerous and/or inaccessible environments that could put human lives at risk. This research demonstrates a consistent way of developing a robust adaptive controller for quad-rotor helicopters, using fuzzy-neural networks; creating an intelligent system that is able to monitor and control the non-linear multi-variable flying states of the quad-rotor, enabling it to adapt to the changing environmental situations and learn from past missions. Firstly, an analytical dynamic model of the quad-rotor helicopter was developed and simulated using Matlab/Simulink software, where the behaviour of the quad-rotor helicopter was assessed due to voltage excitation. Secondly, a 3-D model with the same parameter values as that of the analytical dynamic model was developed using Solidworks software. Computational Fluid Dynamics (CFD) was then used to simulate and analyse the effects of the external disturbance on the control and performance of the quad-rotor helicopter. Verification and validation of the two models were carried out by comparing the simulation results with real flight experiment results. The need for more reliable and accurate simulation data led to the development of a neural network error compensation system, which was embedded in the simulation system to correct the minor discrepancies found between the simulation and experiment results. Data obtained from the simulations were then used to train a fuzzy-neural system, made up of a hierarchy of controllers to control the attitude and position of the quad-rotor helicopter. The success of the project was measured against the quad-rotor’s ability to adapt to wind speeds of different magnitudes and directions by re-arranging the speeds of the rotors to compensate for any disturbance. From the simulation results, the fuzzy-neural controller is sufficient to achieve attitude and position control of the quad-rotor helicopter in different weather conditions, paving way for future real time applications.