Research On Control Strategy And Control Method Of Mach Number For Wind Tunnel

Wind tunnel is the most important equipment in the aerodynamic test. Mach number control system is the assurance of wind tunnel performance evaluation. The control precision, stability and rapidity of wind tunnel Mach number play a decisive role in wind tunnel test and have a direct effect on flow field quality optimization and energy conservation. This paper takes injector powered transonic wind tunnel as the research object, and does the research on wind tunnel Mach number control strategy and control method.On the basis of analysis of injector powered transonic wind tunnel’s aerodynamic structure and wind tunnel test peculiarity,Wind tunnel system flow field’s establishment needs short time and high precision. As wind tunnel has a very complicated structure, it’s difficult to build an accurate mathematical model. All above make wind tunnel control harder. Wind tunnel test could be divided into pressure charging phase and testing phase. Nonlinear degree and coupling degree in pressure charging phase are much higher than those in testing phase, so it’s difficult to achieve ideal control performance when using the same control method. In order to solve this problem, wind tunnel Mach number section control strategy is designed in the light of these two phases’ different properties in this paper.In the pressure charging phase, it needs to establish wind tunnel flow field quickly and stably. Wind tunnel system presents severely non-linear property and strong coupling in this phase, therefore, it’s difficult to achieve desirable performance with traditional control method. In order to solve this problem, a knowledge-based human-simulated intelligent controller is presented in this paper. The controller combines open-loop control and close-loop control, monitors the running state of the system and switch between open-loop time optimal control and close-loop PID control according to human-simulated intelligent control rule, which is determined by prior knowledge and control experience. The controller switches to open-loop control when controlled variable error is large, and switches to close-loop control when controlled variable has small error. Open-loop control variables are selected by case-based reasoning.In the testing phase, wind tunnel control needs swiftness and high stability. As this phase involves various working conditions, it needs to adjust PID parameters repeatedly to meet the control requirement. The adjustment process takes high workload, and it’s hard to guarantee control performance. According to the involved working conditions’ repeatability, a PID controller based on iterative learning approach is presented in this paper. PID parameters optimization is accomplished through the iterative learning approach. Transient performance index is taken as objective function during iterative learning.In order to verify the performance of the controller, a simulation platform is designed and written in LabVIEW in this paper. The simulation results indicate that the knowledge-based human-simulated intelligent controller can effectively reduce the overshoot and regulation time in the pressure charging phase, the PID controller based on iterative learning approach can improve control performance through parameter optimization. The control strategy and control method presented in this paper could satisfy the control requirement. It has a high value of practical application.