132 citations · 330 across the 12 of their papers we have counts for
6 papers · 1 filter
Neural Schrödinger Bridge with Sinkhorn Losses: Application to Data-driven Minimum Effort Control of Colloidal Self-assembly
Iman Nodozi, Charlie Yan, Mira Khare +2
We show that the minimum effort control of colloidal self-assembly can be naturally formulated in the order-parameter space as a generalized Schrödinger bridge problem -- a class o…
A Physics-informed Deep Learning Approach for Minimum Effort Stochastic Control of Colloidal Self-Assembly
Iman Nodozi, Jared O'Leary, Ali Mesbah +1
We propose formulating the finite-horizon stochastic optimal control problem for colloidal self-assembly in the space of probability density functions (PDFs) of the underlying stat…
Safe Exploration and Escape Local Minima with Model Predictive Control under Partially Unknown Constraints
Raffaele Soloperto, Ali Mesbah, Frank Allgöwer
In this paper, we propose a novel model predictive control (MPC) framework for output tracking that deals with partially unknown constraints. The MPC scheme optimizes over a learni…
Receding-horizon Stochastic Model Predictive Control with Hard Input Constraints and Joint State Chance Constraints
Joel A. Paulson, Edward A. Buehler, Richard D. Braatz +1
This article considers the stochastic optimal control of discrete-time linear systems subject to (possibly) unbounded stochastic disturbances, hard constraints on the manipulated v…
Lyapunov-based Stochastic Nonlinear Model Predictive Control: Shaping the State Probability Density Functions
Edward A. Buehler, Joel A. Paulson, Ali Akhavan +1
Stochastic uncertainties in complex dynamical systems lead to variability of system states, which can in turn degrade the closed-loop performance. This paper presents a stochastic…
Stochastic Nonlinear Model Predictive Control with Efficient Sample Approximation of Chance Constraints
Stefan Streif, Matthias Karl, Ali Mesbah
This paper presents a stochastic model predictive control approach for nonlinear systems subject to time-invariant probabilistic uncertainties in model parameters and initial condi…