18 citations · 52 across the 16 of their papers we have counts for
5 papers · 1 filter
Asynchronous Sensitivity-Based Distributed NMPC
Maximilian Pierer von Esch, Andres Völz, Knut Graichen
This paper presents a cooperative distributed model predictive control (MPC) scheme for nonlinear continuous-time systems. The centralized optimal control problem is solved asynchr…
An Overview of Sensitivity-Based Distributed Optimization and Model Predictive Control
Maximilian Pierer von Esch, Andreas Völz, Knut Graichen
This paper presents a concise overview of sensitivity-based methods for solving large-scale optimization problems in distributed fashion. The approach relies on sensitivities and p…
Enforcing Convergence in Sensitivity-based Distributed Programming via Transformed Primal-Dual Updates
Maximilian Pierer von Esch, Andreas Völz, Knut Graichen
Sensitivity-based distributed programming (SBDP) is a decomposition method for solving large-scale nonlinear programs over graph-structured networks. However, its convergence depen…
Sensitivity-Based Distributed Programming for Non-Convex Optimization
Maximilian Pierer von Esch, Andreas Völz, Knut Graichen
This paper presents a novel sensitivity-based distributed programming (SBDP) approach for non-convex, large-scale nonlinear programs (NLP). The algorithm relies on first-order sens…
Sensitivity-Based Distributed Model Predictive Control for Nonlinear Systems under Inexact Optimization
Maximilian Pierer von Esch, Andreas Völz, Knut Graichen
This paper presents a distributed model predictive control (DMPC) scheme for nonlinear continuous-time systems. The underlying distributed optimal control problem is cooperatively…