Study On The Control Of A Electric Control Loading System

As an important section of Flight Simulator, Control Loading system provides force feeling topilots, and meanwhile, it has to do the calculation of the real time rudder angle. The performance ofthe Flight Simulator Control Loading System has a direct infection on the pilots training.Consequently, a high property, high reliability and high fidelity control loading system is a key to ahigh quality Flight Simulator. In allusion to the requirement of the Flight Simulator in the lab,problems of the force servo based Electric Control Loading System are mainly discussed both intheory and experiment in the paper, such as the software and hardware design, system modeling, theforce feeling control and the elimination of extra force.It has an introduction of a PMSM based Electric Control Loading System. It has a personality ofsmall structure, less inputs and flexible control. The force-servo system model, stick force-stickdisplacement model and rudder angle-stick displacement model that needed in the calculation of thereal time rudder angle and the stick force feeling simulation are established. Based upon themathematic models of the system, it has a study on the control of the Electric Control Loading System.It has an analysis of the stability and dynamic characteristics of the system without the considerationof disturbance, and presents a composite control method that has an effective influence on theelimination of the extra force and dynamic characteristics of the system, which has been simulated inMATLAB/SIMULINK. In the PID controller parameter tuning, the MATLAB/NCD model isinnovatively adopted and achieve a good result.For the designed control method, based upon the designed Flight Simulator Electric ControlLoading System prototype for a platform, a test is operated. The testing results verified that thecomposite controller has an effective infection on the dynamic characteristics and precision trackingof the system. But the dead band is emerged. To the dead band, it brings forward a RBF neuralnetwork and PID composite controller. The RBF neural network sets the PID parameters on lineaccording to the identification result of the system model. As a result, the controller will not rely onthe accurate mathematic model of the system and can achieve a real time control of the nonlinearsystem. The simulation and experimental results indicate the favorable control effect of the controller.