Ma Dawei

A Hierarchical Control Strategy for Antiaircraft Multiple Launch Rocket System

A novel three-level hierarchical control strategy for antiaircraft multiple launch rocket system (MLRS) is designed in this paper. In this strategy, expert system is used to provide control signals and parameters to adjust controller performance in organization level. And a compound controller including both sliding-mode neural network and compensator is used to guarantee the accuracy and stability of servo systems in real-time control level. Results show that the control strategy can guarantee the dynamic performance of system.

Notice of Retraction Numerical simulations of the interaction between the shock waves and moving interfaces

It is a crucial problem in the studies of the fluid on numerical simulations of the interfaces movement in multi-component fluids. Fluid dynamic characteristics are studied during shock waves pass helium bubble in this paper. These methods are used including high resolution discontinuous finite method for Fluid dynamic equations, Level set method for capturing moving interfaces and ghost fluid method with Isobaric fix for disposing the interfaces, which obtains profiles of the helium bubble, density contours and the curves of density and pressure with center axial direction. It is in good agreement for the general bubble shape and position compared with those in other numerical schemes.

Application of neural network sliding controller in ac servo systems

Some ac position servo system possesses abominable load properties, i.e. largely changing moment of inertia and moment of imbalance. Its difficult for traditional PID controller to achieve the satisfying effects. In order to realize high speed and high precisive position control of the ac servo system, aiming at uncertainties in actual system, a neural network sliding controller is put forward, which takes the advantages of excellences such as rapid response and consistence for parameters variation and foreign disturbance, and takes advantage of online learning function of neural network to eliminate chattering in sliding control to enhance steady performance of system. Simulation results indicate this controller can restrain disturbance effectively, decrease sensitivity of system to parameters variation, improve dynamic performance and steady precision markedly, fulfill the goal of rapid tracking. It has practical application value.