Supersonic, variable-throat, blow-down wind tunnel control using genetic algorithms, neural networks, and gain scheduled PID

摘要

In this paper the design and application of a control algorithm is discussed to control the test conditions within plenum chamber and the test section of a supersonic blow-down, variable throat wind tunnel at the University of Alabama. The artificially intelligent controller algorithm was designed using a gain scheduled Proportional-Integral-Differential (PID) control approach. The PID controller was augmented to work with time variant properties of the control problem by determining a functional form of the integral term of the controller from the governing equations of the tunnel. The controller was optimized using genetic algorithms (GA) on a neural network (NN) model of the tunnel and was compared to a conventional PID controller using the same NN model. The process was repeated for different throat settings to find the control gains for each setting. The controller algorithm was next applied to the actual wind tunnel at different throat settings and the results were compared. The optimized controller is proven to work very well at every throat setting.