Dissipative Imitation Learning for Discrete Dynamic Output Feedback Control with Sparse Data Sets
This addresses the challenge of stable controller synthesis in robotics or control systems with minimal data, though it is incremental as it builds on existing dissipativity and imitation learning frameworks.
The paper tackles the problem of ensuring stability in imitation-learned controllers with sparse data and limited plant model knowledge, achieving closed-loop stability and successful mimicry of expert behavior where other methods fail.
Imitation learning enables the synthesis of controllers for complex objectives and highly uncertain plant models. However, methods to provide stability guarantees to imitation learned controllers often rely on large amounts of data and/or known plant models. In this paper, we explore an input-output (IO) stability approach to dissipative imitation learning, which achieves stability with sparse data sets and with little known about the plant model. A closed-loop stable dynamic output feedback controller is learned using expert data, a coarse IO plant model, and a new constraint to enforce dissipativity on the learned controller. While the learning objective is nonconvex, iterative convex overbounding (ICO) and projected gradient descent (PGD) are explored as methods to successfully learn the controller. This new imitation learning method is applied to two unknown plants and compared to traditionally learned dynamic output feedback controller and neural network controller. With little knowledge of the plant model and a small data set, the dissipativity constrained learned controller achieves closed loop stability and successfully mimics the behavior of the expert controller, while other methods often fail to maintain stability and achieve good performance.