Neural network control does not have a predefined equation like a PID controller. Instead, it relies on training to learn the control strategy from data. The control action is derived from the network’s learned weights and architecture.
Example Process for Neural Network Control:
- Data Collection: Gather data from the system, including inputs, outputs, and error signals.
- Network Design: Design a neural network architecture suitable for the control task.
- Training: Train the neural network using a dataset that includes input-output pairs, often minimizing a cost function that penalizes error or deviation from desired behavior.
- Deployment: Use the trained neural network in a control loop, where it receives inputs (such as the current state or error) and outputs a control signal.
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