| Summary: | 碩士 === 國立臺灣科技大學 === 電機工程系 === 102 === In this thesis, the actual train of Taipei Rapid Transit Systems (TRTS) is modeled with MATLAB/ Simulink. It consists of three main parts which are traction system, third rail voltage and the load. The traction system consists of induction motor with variable voltage variable frequency (VVVF) control, switching modes (SPWM, Quasi Six-step, and Sis Step), and three phase bridge inverter. The load consists of starting resistance, the running resistance during train movement, and gradient and curve resistances from the track profile. Four operation modes are used in the speed regulation which are acceleration, cruising, coasting, and braking.
Back Propagation Neural Network (BPNN) based PID is used as speed controller. This algorithm is the combination of Neural Network and PID. This technique makes PID tuning automatically since the tuning is done by the Neural Network. On-line learning capability of neural network can make the controller more adaptable.
Two case studies are presented to prove that the controller can work well regardless the gradient changes or load variations. The acceleration and jerk are limited within ± 1 m/s2 and ± 1 m/s3, respectively. Jerk limit ± 0.8 m/s3 which according to the TRTS regulation also implemented. Regenerative braking and coasting mode are successfully implemented to reduce the energy consumption.
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