Asset Avatar Reinforcement Learning for Adaptive Controller Tuning

The system performs preliminary actions by training the neural network controller extensively in the digital twin environment before deployment. This pre-training phase allows the controller to learn optimal control strategies for various operating conditions without consuming actual production time, so that when deployed to physical equipment, it requires minimal or no re-tuning.

The patent creates a digital twin (copy) of the physical equipment that replicates its dynamics and behavior. The neural network controller is trained on this digital copy rather than the actual physical equipment, allowing iterative learning and tuning to be performed virtually. This copying approach eliminates the need for time-consuming trial-and-error tuning on real equipment while maintaining training effectiveness.

The patent implements dynamics by transitioning from fixed parameters to adaptive parameters. The neural network controller dynamically adjusts its parameters based on real-time sensor data from the equipment. The controller's weights and biases are not static but can be updated online as the equipment operates, allowing the controller to adapt to changing conditions while maintaining a relatively simple overall structure.

The neural network controller performs self-service by automatically adjusting its own parameters based on feedback from the equipment. Through online learning mechanisms, the controller can retrain or fine-tune itself using data from the digital twin or direct sensor inputs, enabling it to adapt to equipment degradation, environmental changes, or operational variations without external intervention.