Inverse Learning-Based Output Feedback Control of Nonlinear Systems with Verifiable Guarantees

Researchers from Seoul National University and the University of Texas at Austin have developed a breakthrough data-driven control method for nonlinear systems that provides verifiable performance guarantees. The team's "Inverse Learning-Based Output Feedback Control" approach, detailed in their 17-page arXiv paper, uses kernel interpolation to create an inverse model from input/output measurement data, then actively selects appropriate reference outputs from the same dataset. This eliminates the need for complete mathematical models of complex systems while maintaining provable stability.

The controller operates through two key components: an inverse model that maps desired outputs and current states to required control inputs, and a data-driven reference selection framework that chooses suitable desired outputs from the identification dataset. The researchers established verifiable sufficient conditions on the dataset that guarantee practical output regulation, with numerical simulations demonstrating effectiveness across 5 different scenarios. Additional evaluations tested robustness against output measurement noise, showing the method's practical applicability to real-world control problems where perfect measurements are impossible.

This research represents a significant advance in bridging the gap between data-driven machine learning approaches and traditional control theory's requirement for mathematical guarantees. By providing verifiable conditions for stability and performance, the method addresses a critical limitation of many AI-based control systems that operate as "black boxes" without safety assurances. The approach could enable more reliable deployment of AI controllers in safety-critical applications like autonomous vehicles, robotics, and industrial automation systems.