Dynamic modelling and control of a flexible manoeuvring system.

In this research a twin rotor multi-input multi-output system (TRMS), which is a laboratory platform with 2 degrees of freedom (DOF) is considered. Although, the TRMS does not fly, it has a striking similarity with a helicopter, such as system nonlinearities and cross-coupled modes. Therefore, the TRMS can be perceived as an unconventional and complex "air vehicle" that poses formidable challenges in modelling, control design and analysis, and implementation. These issues constitute the scope of this research. Linear and nonlinear models for the vertical movement of the TRMS are obtained via system identification techniques using black-box modelling. The approach yields input-output models without a priori defined model structure or specific parameter settings reflecting any physical attributes of the system. Firstly, linear parametric models, characterising the TRMS in its hovering operation mode, are obtained using the potential of recursive least squares (RLS) estimation and genetic algorithms (GAs). Further, a nonlinear model using multi-layer perceptron (MLP) neural networks (NNs) is obtained. Such a high fidelity nonlinear model is often required for nonlinear system simulation studies and is commonly employed in the aerospace industry. Both time and frequency domain analyses are utilised to investigate and develop confidence in the models obtained. The frequency domain verification method is a useful tool in the validation of extracted parametric models. It allows high-fidelity verification of dynamic characteristics over a frequency range of interest. The resulting models are utilized in designing controllers for low frequency vibration suppression, development of suitable feedback control laws for set-point tracking, and design of augmented feedforward and feedback control schemes for both vibration suppression and set-point tracking performance. The modelling approaches presented here are shown to be suitable for modelling complex new generation air vehicles, whose flight mechanics are not well understood. Modelling of the TRMS revealed the presence of resonance modes, which are responsible for inducing unwanted vibrations in the system. Command shaping 11 control strategies are developed to reduce motion and uneven mass induced vibrations, produced by the main rotor during the vertical movement around the lateral axis of the TRMS rig. 2-impulse, 3-impulse and 4-impulse sequence input shapers and Iow-pass and band-stop digital filters are developed to shape the command signals such that the resonance modes are not overly excited. The effectiveness of this concept is then demonstrated in both simulation and real-time experimental environments in terms of level of vibration reduction using power spectral density profiles of the system response. Combinations of intelligent and conventional techniques are commonly used the control of complex dynamic systems. Such hybrid schemes have proved to be efficient and can overcome the deficiencies of conventional and intelligent controllers alone. The current study is confined to the development of two forms of hybrid control schemes that combine fuzzy control and conventional PID compensator for input tracking performance. The two hybrid control strategies comprising conventional PO control plus PlO compensator and PO-type fuzzy control plus PlO compensator are developed and implemented for set-point tracking control of the vertical movement of the TRMS rig. It is observed that the hybrid control schemes are superior to other feedback control strategies namely, PlO compensator, pure PO-type and PI-type fuzzy controllers in terms of time domain system behaviour. This research also witnesses investigations into the development of an augmented feedforward and feedback control scheme (AFFCS) for the control of rigid body motion and vibration suppression of the TRMS. The main goal of this framework is to satisfy performance objectives in terms of robust command tracking, fast system response and minimum residual vibration. The developed control strategies have been designed and implemented within both simulation and real-time environments of the TRMS rig. The employed control strategies are shown to demonstrate acceptable performances. The obtained results show that much improved tracking is achieved on positive and negative cycles of the reference signal, as compared to that without any control action. The system performance with the feedback controller is significantly improved when the feedforward control component is added. This leads to the conclusion that augmenting feedback control with feedforward method can lead to more practical and accurate control of flexible systems such as the TRMS