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Simulation Study On Electric Load Simulator Control

Posted on:2010-06-19Degree:MasterType:Thesis
Country:ChinaCandidate:J SongFull Text:PDF
GTID:2178360275485581Subject:Measuring and Testing Technology and Instruments
Abstract/Summary:PDF Full Text Request
The electric loading system is the equipment for rudder's laboratory test. It is used to simulate the torque load of aircraft's rudder system and examine its actual performance. With the requirements of national defense, the performance and flight speed of missiles and aircrafts are increased rapidly, which leads to the high dynamic responsibility and high precision requirement of the load simulator. Control strategy issue of electric load simulator is analyzed theoretically and studied by simulation.Mathematical model was based on the design of control algorithms, the mathematical model of the electrical load simulation was built using the mechanism modeling method firstly, taken the control system design of electrical load simulation drive by PMSM directly as the background. Frequency response characteristics of the key blocks in system module was tested by experimental methods as verification in modeling process. Based on system mathematical model, system's dynamics was analyzed. To improve the stability of system, differential torque feedback was imported. The influence of the surplus torque was studied by way of simulation, and it was found that surplus torque affects the system greatly. According to the characteristics of load system, the PID algorism was proposed so that output can track input accurately, and adopts forward feed compensation principle as compensation to diminished surplus torque disturbance. To overcome limitation of traditional control method, PID control strategy based on RBF neural network was proposed. The results of simulation indicated that the control strategy was effective, the control system's dynamic performance were improved remarkably compared with the pure classical control.
Keywords/Search Tags:electrical load simulation, surplus torque, PID control, forward feed compensation, RBF neural network
PDF Full Text Request
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