Neural computing: theory and practice
Neural computing: theory and practice
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Modern Control Engineering
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Practical Applications of Computational Intelligence for Adaptive Control
Practical Applications of Computational Intelligence for Adaptive Control
Genetic Tuning of PID Controllers Using a Neural Network Model: A Seesaw Example
Journal of Intelligent and Robotic Systems
Genetic Optimization of Variable Structure PID Control Systems
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
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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.