Our novel controller uses machine learning and artificial intelligence methods to improve standard industrial control, including robotics. These patented techniques build on decades of research from MIT into efficient methods for building robust adaptive controllers for nonlinear systems.
The controller can continue to adapt while in use to account for unknown or expected deviations from original tuning conditions, whether that’s because of wear and tear on the plant, or a change in environmental dynamics. Best of all, we can implement the entire controller on state-of-the-art, efficient neuromorphic hardware.
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