Hardware ML Control Channels for Real-Time Redundant Output

The patent combines multiple control functions into a single controller by implementing multiple independent control channels within one device. Each channel processes inputs independently through its own machine learning model and feature creation module, providing redundancy without requiring separate physical controllers. This merging approach maintains failure mitigation capabilities while avoiding the cost scaling issue of multiple independent controllers.

The patent replaces traditional signal processing techniques (mechanical/system-based filtering) with machine learning-based processing. The machine learning models process inputs and generate outputs without requiring temporal calculations or filtering operations that introduce delay. This substitution eliminates response delay while maintaining the ability to produce refined, high-quality outputs through the intelligent processing capabilities of the machine learning models.

The patent substitutes software-based control functions with hardware-based machine learning models. The machine learning models are implemented in hardware circuitry rather than software, eliminating the vulnerability to software threats and cyber attacks. The adaptability is maintained through the machine learning models' ability to process various inputs and generate appropriate control outputs, while the hardware implementation provides inherent security against software-based exploits.

The patent segments the controller into multiple completely independent control channels, where each channel has its own separate machine learning model, feature creation module, and processing path. This segmentation ensures that failures in one channel do not propagate to other channels, eliminating common-mode failures caused by shared components or correlated processing. Each channel operates autonomously, providing true diversity in the redundancy implementation.