Research On Control Strategy Of Load Simulator For Gun Control Device

With the development of National Defense Science and Technology Industry, the accuracy requirement and automation level of the gun control system is also higher and higher. The servo system, as a very important part of gun control system, directly affects the performance of the whole system, so we need test the servo system performance during the process of development. The load simulator in this paper is used to simulate the workload environments of gun control system to test the performance of the servo system, and made an intensive study of identification and strategies by analyzing the extra torque and nonlinear characteristics of the load simulator.This paper introduces basic structure and working principle of the electric-driven load simulator, and then establishes the mathematical model of permanent magnet synchronous motor, torque sensor, rotating inertia plate and the whole system, the extra torque, uncertainties and nonlinearity of the load system are analyzed in detail, which laying a good foundation for system identification and control strategy.The off-line training and online adjustment identification method is adopted. RBF neural network is used to identify system, considering the problem of parameters of RBF neural network, an improved genetic algorithm is used to optimize the centers and widths, and obtain the weight by using least squares algorithms, the off-line trained parameters is regard as the initial value of the online identifier, the vibration phenomenon is avoided, and accelerate up the convergence speed.In order to restrain the extra torque of the electric-driven load simulator, a kind of feed-forward compensation controller based on Structure Unchangeable Principle is designed, the simulation demonstrates the shortcomings of the controller based on precision model; A kind of adaptive PID controller based on improved genetic algorithm and RBF neural network is adopted, combine genetic algorithm and RBF neural network with PID controller, which exerts the advantage of each other.Setup the semi-physical simulation platform, by which the control strategy is verified. Experimental results show that the method can restrain the extra torque and has better robustness, the control accuracy is guaranteed.