Intelligent systems for active vibration control in flexible engineering structures / by Ji Xiaoxu.

Vibration arises in almost all moving structures. Vibration control is important to many applications such as robotic arms, aircraft wings, buildings in wind, vehicle transmission systems,to name but a few. The objective of this thesis is to develop more efficient intelligent controllers for vibration suppression, mainly for time-varying flexible structures. At first, based on TSO and TSl fuzzy models, novel neural-fuzzy (NF) controllers are developed for active vibration control of the flexible structures. The NF control paradigms are intended to integrate the advantages from both fuzzy logic and neural networks while overcoming their respective limitations. The control reasoning is undertaken by fuzzy logic whereas the fuzzy control system is optimized by neural network related training algorithms. A new strategy is suggested to simplify the architectures of the classical NF controllers so as to make the control process computationally efficient for real-time applications. A recurrent identification network (RIN) is developed to adaptively identify system dynamics of the timevarying flexible structures. When system dynamics (e.g., mass, stiffness, and damping) varies, the proposed RIN and NF controller can effectively recognize the system’s new dynamics and perform corresponding control operations. A novel hybrid training technique based on real time recurrent learning (RTRL) and least square estimate (LSE) is suggested for real-time training of the RIN scheme to optimize its nonlinear input-output mapping. The effectiveness of the developed intelligent controllers and the related techniques has been verified by online experimental tests of corresponding fixed and time-varying dynamic conditions. Test results have shown that the developed adaptive NF controller outperforms the classical controllers (e.g., PD) and other related intelligent control strategies

Demand-side management in office buildings in Kuwait through an ice-storage assisted HVAC system with model predictive control

Examining methods for controlling the electricity demand in Kuwait was the main objective and motivation of this researchp roject. The extensiveu se of air-conditioning for indoor cooling in office and large commercial buildings in Kuwait and the Gulf States represents a major part of the power and electricity consumption in such countries. The rising electricity generation cost and growing rates of consumption continuously demand the construction new power plants. Devising and enforcing Demand-SideM anagemen(t DSM) in the form of energye fficient operations trategies was the response of this research project to provide a means to rectify this situation using the demand-side management technique known as demand levelling or load shifting. State of the art demand-sidem anagementte chniquesh ave been examined through the developmenot f a model basedp redictive control optimisations trategyf or an integrateda ndm odulara pproachto the provisiono f ice thermals torage. To evaluate the potential of ice-storage assisted air-conditioning systems in flattening the demand curve at peak times during the summer months in Kuwait, a model of a Heating, Ventilation, and Air-conditioning (HVAC) plant was developed in Matlab. The model engaged the use of model based predictive control (MPQ) as an optimisation tool for the plant as a whole. The model with MPC was developed to chose and decide on which control strategy to operate the integrated ice-storage HVAC plant. The model succeeded in optimising the operation of the plant and introduced encouraging improvement of the performance of the system as a whole. The concept of the modular ice-storage system was introduced through a control zoning strategy based on zonal orientation. It is believed that such strategy could lead to the modularisation of ice-storage systems. Additionally, the model was examined and tested in relation to load flattening and demonstrated promising enhancement in the shape of the load curve and demonstrated flattened demand curves through the employed strategy. When compared with measured data from existing buildings, the model showed potential for the techniques utilised to improve the load factor for office buildings.EThOS - Electronic Theses Online ServiceGBUnited Kingdo