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Found 3 papers, 0 papers with code
Dissipative Imitation Learning for Discrete Dynamic Output Feedback Control with Sparse Data Sets
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.
Iterative, Small-Signal L2 Stability Analysis of Nonlinear Constrained Systems
This paper provides a method to analyze the small-signal L2 gain of control-affine nonlinear systems on compact sets via iterative semi-definite programs.
Failing with Grace: Learning Neural Network Controllers that are Boundedly Unsafe
As a result, our method can learn a safe vector field for the closed-loop system and, at the same time, provide worst-case bounds on safety violation over the whole configuration space, defined by the overlap between the over-approximation of the forward reachable set of the closed-loop system and the set of unsafe states.
