Abstract:
Magnetic levitation technology has been receiving increasing attention because it helps eliminate frictional losses due to mechanical contact. A Magnetic Levitation System (MLS) is considered as a good test-bed for the design and analysis of control systems since it is a nonlinear unstable plant. Controller design for such unstable system involves optimizing various parameters, such as settling time, peak overshoot etc. Since the system is very prone to disturbances, design must ensure robustness and disturbance rejection performance.
The aim of the thesis is to develop various controlling schemes, which deal with the uncertainties of the system, maintaining optimum time specifications. In order to achieve
• this, first the System model has been designed. With understanding of system performance, initially the use of conventional methods to control system dynamics employed. Later, Improvement in controlling techniques is performed. It involved employing Proportional-Integral-Derivative (PID) control, followed by application of soft computing techniques including fuzzy logic, genetic algorithms, neuro-computing and particle swarm optimization(PSO).
Firstly, tuning capabilities of genetic algorithm and particle swarm optimization technique for a PID controller are explored. Secondly, fuzzy logic systems are developed based on behavior of system, which is combined with tuning of scaling parameters with genetic algorithm. These applications are analyzed on magnetic levitation system and results showed the capability of proposed algorithms.
