Innovative and Efficient Electric Braking System In High-Speed Trains using Proportional Resonant Controller
Saranu Ravikumar

Saranu Ravikumar*, Assistant Professor, Department of Mechanical Engineering, Bapatla Engineering College, Bapatla, Andhra Pradesh, India.

Manuscript received on November 14, 2019. | Revised Manuscript received on 22 November, 2019. | Manuscript published on December 10, 2019. | PP: 1691-1698 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7551129219/2019©BEIESP | DOI: 10.35940/ijitee.B7551.129219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The high-speed trains are eight times more efficient than traditional trains because it significantly operates faster than the other trains; however, the train accidents are happened as because of its poor braking system. From this reason, effective braking system control techniques are developed. In this paper, the electric brake regenerative system is introduced to control the high-speed train. Therefore the braking system of a high-speed train is modeled in Brushless Direct Current (BLDC) motor, which is controlled by the gain of Proportional Resonant (PR) controller. Subsequently, the parameters of the controller and error percentage from the controller in the braking system are optimized using Multi-Objective African Buffalo Optimization (MOABO). The developed controller in braking system stability is analyzing by the Lyapunov function. The results of the braking system are validating by the torque and speed of the high-speed train braking system. Furthermore, the proposed high-speed braking system control is compared with existing control techniques in a high-speed train. 
Keywords: Braking System, Brushless Direct Current Motor, Current Controller, Stability Analysis.
Scope of the Article: Innovative Sensing Cloud and Systems