Research On Identification And Control Strategy Of Long-Range Multi-Barrel Rocket Launcher Electro-Hydraulic Position Servo System

Currently, the technology of long-distance firepower in our army still lags behind that of these developed countries. Therefore, it holds great significance to work on the new long-range multi-barrel rocket launcher .The performance of the position servo system of rocket launcher is a bottleneck , which restrict the firing accuracy and reaction rates of rocket launcher .In order to develop the new rocket launcher , the study of the new position servo system becomes important . Based on the development of a new long-range multi-barrel rocket launcher, present work mainly focuses on its identification and control strategy of the electro-hydraulic position servo system of pump-controlled cylinder (EPSSPC).The study reasons transfer function for EPSS based on the detailed analysis of the structure and working principle of EPSSPC. Using SimMchanics and SimHydraulics toolbox in MATLAB, a new simulation model of this system is proposed. The nonlinear and time-variation factors existing in the present system are analyzed, which paves the way for the controlling study and experimental analysis in the next stage.This study also introduces system identification scheme of "off-line training first, on-line minor adjustment following". When conducting off-line identification, this paper first uses the genetic algorithm to optimize the values of weights and thresholds, and then obtains an optimized initial value. Then the BP algorithm is applied to optimize at a negative gradient direction to find out an optimal values of weights and thresholds. The method of BP neural network based on genetic algorithm characterized by higher identification accuracy is employed, which offers a better solution to the problem of being prone to fall into local minimum in BP neural network . The method also avoids the oscillation in using on-line minor adjustment by keeping weight after off-line training a reasonable value. When conducting on-line identification, the fast BP algorithm containing the accessional momentum and adaptive learning rate is adopted, which accelerates the convergence of BP algorithm, makes it perform better in the on-line identification study.Furthermore, this work advances the neural network model reference adaptive control (NNMRAC) method of EPSSPC. The mathematic model of the known process plant is needed in the back propagation of the neural network controller, therefore it is very difficult to proceed the learning and modifying of neural network controller in the present system with nonlinear and time-variations features. To solve this problem, the NNMRAC scheme with the on-line neural network identifier is presented. The on-line neural network identifier offers real-time gradient information for the neural network controller, which guarantees the proper learning and modification of the controller.Moreover, this study proposes the adaptive fuzzy sliding mode control scheme of EPSSPC, in which the difficulty in directly obtaining the equivalent controller due to external disturbance and parameters uncertainties is solved by using adaptive fuzzy system to approach equivalent controller. Two means are adopted to solve the chattering reduction problem commonly found in the sliding mode variable structure control: (1) the thickness of the boundary layer is tuned online by a fuzzy system which is designed based on the chattering variable and the absolute value of the switching function; (2) the control gain is tuned online by a fuzzy system which is designed based on the switching function and its variation.Finally, the hardware circuit and controller-software are designed and the simulation experiments are conducted on the semi-physical simulation test bench, the result of which proves the accuracy of the theory and the simulation in this work, offering a reference theoretically to develop the prototype of the system in the future.