This thesis presents investigations into development of modelling and control of flexible structures using swarm intelligence optimisation techniques. A smart flexible beam structure is used in this work as a candidate application. The smart flexible beam model is developed using finite difference method and a methodology of incorporating piezoelectric patch actuator into finite difference model is presented. The simulation model is developed in MATLAB/SIMULINK environment as a platform for test and verification of the control approaches developed in this work. Many heuristic search algorithms have been inspired by nature such as genetic algorithm (natural evolution), artificial neural network (biological neuron) and artificial immune system (immune system) where the algorithms try to mimic the biological process. Addition to nature inspired algorithms is the swarm intelligence method which has been inspired by the natural behaviour of a group of insects like foraging, flocking and schooling in ants, bees, fish and birds where particle swam optimisation (PSO) and ant colony optimisation (ACO) are the most popular methods. The study of parameter setting for PSO and continuous ACO (ACOr) is studied through parametric modelling of the beam. The performance of each algorithm in terms of computational time and convergence is discussed. In this study, vibration control of a flexible beam structure is developed based on the principle of wave interference, to result in optimal cancellation with adaptive model-based control and adaptive direct control. A single objective optimisation algorithm is developed and implemented using PSO and continuous ACO considering two conditions; optimisation of controller with pre-selected location of sensor and actuator and simultaneous optimisation of controller parameters and sensor and actuator location in single-input-single-output and single-input-multiple-output configurations. While single objective optimisation provides only one solution, the use of multi-objective optimisation results in several solutions to choose for implementation. An approach of multi-objective optimisation of controllers' parameters and sensor/actuator location is developed based on minimising vibration energy and minimising actuator force. Multi-objective PSO and multi-objective ACOr algorithms are developed in finding optimal system setup and controller parameters for AYC of the beam. Both PSO and ACO based algorithms are tested and their performances assessed in vibration control of the beam.
|Publisher||University of Sheffield|
|Source Sets||Ethos UK|
|Type||Electronic Thesis or Dissertation|
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