activity
20232026
most citedForward Invariance in Neural Network Controlled Systems

5 citations · 15 across the 17 of their papers we have counts for

collaborators
Showing 2024Show all

5 papers · 1 filter

eess.SY2024

A Linear Differential Inclusion for Contraction Analysis to Known Trajectories

Akash Harapanahalli, Samuel Coogan

Infinitesimal contraction analysis provides exponential convergence rates between arbitrary pairs of trajectories of a system by studying the system's linearization. An essentially…

eess.SY2024

A Global Coordinate-Free Approach to Invariant Contraction on Homogeneous Manifolds

Akash Harapanahalli, Samuel Coogan

In this work, we provide a global condition for contraction with respect to an invariant Riemannian metric on reductive homogeneous spaces. Using left-invariant frames, vector fiel…

cs.LG2024★ 1 cited

Certified Robust Invariant Polytope Training in Neural Controlled ODEs

Akash Harapanahalli, Samuel Coogan

We propose a framework for training neural network controllers with certified robust forward invariant polytopes. First, we parameterize a family of lifted control systems in a hig…

eess.SY2024

Efficient Reachable Sets on Lie Groups Using Lie Algebra Monotonicity and Tangent Intervals

Akash Harapanahalli, Samuel Coogan

In this paper, we efficiently compute overapproximating reachable sets for control systems evolving on Lie groups, building off results from monotone systems theory and geometric i…

eess.SY2024

: A Parallelizable and Differentiable Toolbox for Interval Analysis and Mixed Monotone Reachability in JAX

Akash Harapanahalli, Saber Jafarpour, Samuel Coogan

We present an implementation of interval analysis and mixed monotone interval reachability analysis as function transforms in Python, fully composable with the computational framew…