Generalist AI Controller Adapts Across Diverse System Dynamics

A significant advancement in control theory has been achieved with the introduction of a Generalist Controller, an AI-driven system designed to manage a wide array of dynamic systems using a single, unified policy. Unlike traditional controllers that are custom-built for specific systems, this new approach employs a novel dynamic state-space representation combined with attention mechanisms, allowing a single neural network to adapt to systems of different dimensions without requiring architectural changes. The controller was trained on a massive dataset comprising over 300,000 demonstrations from 25 distinct systems, including complex linear and nonlinear dynamics found in aerospace, underwater vehicles, and chemical processes. Simulation results demonstrate that this generalist approach matches the performance of specialized LQI controllers across all tested systems, even those with challenging non-minimum-phase and unstable dynamics. Crucially, it also exhibits strong generalization capabilities when faced with unseen operating conditions such as actuator saturation, noise, disturbances, and novel reference trajectories, marking a substantial step towards truly adaptive and multi-purpose control algorithms.