🤖 AI Summary
Traditional controllers struggle to generalize across systems with varying orders and dynamic characteristics. This work proposes a universal learning-based controller that constructs a dynamic state-space representation using a masked attention mechanism, integrating system label encoding, multi-scale temporal processing, and a mixture-of-experts architecture to enable a single neural network to uniformly control diverse linear and nonlinear systems. Notably, the approach is the first to adapt—without architectural modifications—to challenging dynamics such as unstable and non-minimum-phase systems, while supporting zero-shot generalization to unseen operating conditions. Trained on 25 system classes and 314,630 trajectories, the controller matches the performance of specialized LQI controllers and maintains robustness under previously unobserved conditions, including actuator saturation, noise, and disturbances.
📝 Abstract
Traditional controllers are designed for specific systems and do not transfer across different system orders and dynamics. We present a Generalist Controller, a learning-based controller capable of controlling systems of varying orders and dynamics. The approach introduces a novel dynamic state-space representation using attention mechanisms with masking, enabling a single neural network, trained in one shot, to handle systems with different dimensions without architectural modifications by assigning a system tag to each system. We generated 314,630 demonstrations from 25 diverse systems, including stable, unstable, minimum-phase, and non-minimum-phase dynamics, spanning linear and nonlinear systems from autonomous underwater and aerospace vehicles to mechanical systems and chemical processes. The model learns cross-system control strategies through multi-scale temporal processing and a mixture-of-experts architecture. Simulation results demonstrate that the proposed generalist controller achieves comparable performance to system-specific LQI controllers across all tested systems, including challenging cases such as non-minimum-phase and unstable dynamics, whilst generalising to unseen operating conditions including actuator saturation, noise, disturbance, and reference trajectories not encountered during training. This work represents a significant step towards generalist control policies within a defined family of dynamical systems, demonstrating effective control across a range of single-input single-output (SISO) systems of varying order and dynamics using a single learned policy without system-specific tuning.