الفهرس | Only 14 pages are availabe for public view |
Abstract Optimization is one of the most discussed topics in engineering and applied research. The last decade has witnessed a great interest in using evolutionary algorithms (EAs) such as genetic algorithms (GA) and Particle Swarm Optimization (PSO), for multivariate optimization. This thesis presents a modern approach of speed control for three-phase induction motor (IM) using PSO algorithm to optimize the parameters of Proportional-Integral (PI) and Hybrid Control (HC) which is a combination between PI controller and Fuzzy Logic Controller (FLC). Also, comparison between different controllers is achieved, using PI controller and HC which are tuned by two methods; firstly a conventional fixed (Trial-and-Error) and secondly using PSO technique. Hybrid of Fuzzy Logic (FL) and PI controller PSO-based for the speed control of given motor is also performed to eliminate the drawbacks of PI speed controller (overshoot, undershoot) and FLC (steady-state error). Also, The FLC is used to overcome parameter variations, sudden speed or load changes and other non-linear disturbances. A minimum number of fuzzy rules and membership functions (MFs) are proposed in this thesis to reduce computational burden time as well as complexity. This work employs also a technique called Field-Oriented Control (FOC) of IM to achieve high performance of the drive. Also, this thesis proposed a Gain-Scheduling Adaptive of a Proportional- Integral (GSAPI) controller scheme for speed control of IM drives using a PSO algorithm to optimize the PI parameters. The PI gains are allowed to vary within a predetermined range and therefore, eliminate the problems faced by the conventional fixed PI and the fixed PSO-PI without gain-scheduling control. The performance of the GSAPI speed controller is simulated and compared with the conventional fixed PI and fixed PSO-PI speed controllers under different operating conditions. A prototype of the proposed system is laboratory implemented and controller is installed using a low cost digital signal processor (DSP 1104) in order to approve the simulation results for different dynamic operating condition. |