Soft Supervisory Control of Heterogeneous Dynamical Systems Using Attention Mechanisms

Abstract

Generalist Controllers (GCs) have recently emerged as a promising paradigm for controlling heterogeneous dynamical systems using a single learned policy. However, existing GC approaches typically require prior knowledge of the active system, which limits autonomous deployment and prevents fully self-contained operation. This paper proposes a soft supervisory control framework that employs attention-based trajectory analysis to automatically infer the system currently under control and provide this information to a switching controller. The proposed architecture processes histories of closed-loop states and control actions using state and action encoders, dual-timescale Long Short-Term Memory (LSTM) networks, and a multi-head attention mechanism to extract discriminative temporal features. A supervisory decision layer then estimates the most likely system identity together with a confidence measure which enables automatic controller conditioning. The framework is trained using expert trajectories generated by Linear Quadratic Integral (LQI) controllers and evaluated on five heterogeneous systems spanning stable, unstable, non-minimum-phase, linear, and nonlinear dynamics, which is unique. The obtained results show an overall supervisory classification accuracy of 98.93%, with precision, recall, and F1-scores exceeding 0.97 for all systems. Furthermore, the proposed approach remains effective under measurement noise, successfully identifies systems during transitions, and generalises to different reference signals not encountered during training. These results indicate the potential of attention-based soft supervisory mechanisms to enable autonomous operation in both GCs and classical switching controllers.