Neural Network PID Control Example for Helicopter Pitch Axis

In this example, we demonstrate the application of a neural network PID controller for helicopter pitch angle control. The controller manages the helicopter's pitch axis, which is a critical task directly affecting aircraft stability and maneuverability. The implementation uses a second-order transfer function as the mathematical model that accurately describes the dynamic characteristics of the helicopter pitch axis. The neural network PID controller combines the learning capabilities of neural networks with the stability of traditional PID control. Implementation typically involves creating a neural network structure with three input neurons (proportional, integral, and derivative errors) and a single output neuron (control signal). The backpropagation algorithm is commonly used for online weight adjustment, allowing the controller to adapt to varying flight conditions. Key code components include: - System identification for the second-order transfer function parameters - Neural network architecture definition (number of hidden layers, activation functions) - Real-time error calculation and weight update mechanisms - Stability margin verification through simulation This hybrid approach enables better handling of the challenges in helicopter pitch control, achieving more accurate angle tracking while maintaining system stability across different operating conditions. The controller continuously optimizes its parameters through machine learning, improving performance without manual tuning.