OPTIMIZATION SIMULATION OF LOCOMOTIVE SEMI-ACTIVE SUSPENSION CONTROL BASED ON FUZZY CONTROL AND DETECTION OF ELECTROMECHANICAL EQUIPMENT

Abstract

- The key technology for accelerating locomotives and enhancing their stability is vibration control for vehicles. This paper analyzes the characteristics of passive suspension, active suspension, and semi-active suspension, and comes to the conclusion that semi-active suspension should be the preferred control mode for high-speed train suspension system in China due to its advantages of low energy consumption, simple control, and good failure-oriented safety. The goal of this paper is to improve the ride comfort and running stability of rolling stock, as well as the performance of suspension system. Meanwhile, this paper applies fuzzy control theory to semi-active suspension control based on the characteristics of rolling stock suspension systems, designs a fuzzy control system in accordance with the fuzzy control principle, creates a semi-active suspension model to implement this control system, and implements the control simulation of semi-active suspension system in MATLAB environment. This paper also analyzes the condition monitoring data gathered by mechanical and electrical equipment while it is operating, with the goal of researching the detection of locomotive equipment health conditions. It then extracts characteristic parameters based on the condition monitoring data gathered by sensors at a specific time. The condition monitoring data and the health state of electromechanical equipment are mapped using the adaptive neuro-fuzzy inference system to monitor the health status of electromechanical equipment. According to the simulation results, the semi-active suspension fuzzy control may moderate the suspension's dynamic deformation fluctuation, lessen the wheels' dynamic load, and lower the acceleration of the car's body. The goal of this paper is to improve the performance of the semi-active suspension system by optimizing the membership function and fuzzy control rules of fuzzy controller.