Neural Network-Based PID Control: Neural Network as Controller Rather Than PID Parameter Tuner
- Login to Download
- 1 Credits
Resource Overview
Neural network-based PID control does not use neural networks to tune PID parameters; instead, it employs a neural network directly as the controller, adjusting PID parameters indirectly by training the neural network's weight coefficients through backpropagation and optimization algorithms.
Detailed Documentation
In this approach, neural network-based PID control does not utilize neural networks to adjust PID parameters directly. Rather, the neural network itself serves as the controller, and PID parameters are adjusted indirectly by training the neural network's weights. This method leverages the learning capability and adaptability of neural networks to flexibly tune the performance of PID control systems, enabling better adaptation to varying control requirements and environmental conditions. By implementing the neural network with activation functions and training it using gradient descent or other optimization techniques, this approach can achieve higher control precision and stability, thereby improving the overall system performance.
- Login to Download
- 1 Credits