Advanced model predictive control framework for autonomous intelligent mechatronic systems: A tutorial overview and perspectives
Y Shi, K Zhang - Annual Reviews in Control, 2021 - Elsevier
This paper presents a review on the development and application of model predictive
control (MPC) for autonomous intelligent mechatronic systems (AIMS). Starting from the …
control (MPC) for autonomous intelligent mechatronic systems (AIMS). Starting from the …
Embedded optimization methods for industrial automatic control
Starting in the late 1970s, optimization-based control has built up an impressive track record
of successful industrial applications, in particular in the petrochemical and process …
of successful industrial applications, in particular in the petrochemical and process …
Fusion of machine learning and MPC under uncertainty: What advances are on the horizon?
This paper provides an overview of the recent research efforts on the integration of machine
learning and model predictive control under uncertainty. The paper is organized as a …
learning and model predictive control under uncertainty. The paper is organized as a …
A software framework for embedded nonlinear model predictive control using a gradient-based augmented Lagrangian approach (GRAMPC)
T Englert, A Völz, F Mesmer, S Rhein… - Optimization and …, 2019 - Springer
A nonlinear MPC framework is presented that is suitable for dynamical systems with
sampling times in the (sub) millisecond range and that allows for an efficient implementation …
sampling times in the (sub) millisecond range and that allows for an efficient implementation …
An inexact augmented Lagrangian framework for nonconvex optimization with nonlinear constraints
MF Sahin, A Alacaoglu, F Latorre… - Advances in Neural …, 2019 - proceedings.neurips.cc
We propose a practical inexact augmented Lagrangian method (iALM) for nonconvex
problems with nonlinear constraints. We characterize the total computational complexity of …
problems with nonlinear constraints. We characterize the total computational complexity of …
Optimization of energy consumption of industrial robots using classical PID and MPC controllers
R Benotsmane, G Kovács - Energies, 2023 - mdpi.com
Industrial robots have a key role in the concept of Industry 4.0. On the one hand, these
systems improve quality and productivity, but on the other hand, they require a huge amount …
systems improve quality and productivity, but on the other hand, they require a huge amount …
Iteration complexity of inexact augmented Lagrangian methods for constrained convex programming
Y Xu - Mathematical Programming, 2021 - Springer
Augmented Lagrangian method (ALM) has been popularly used for solving constrained
optimization problems. Practically, subproblems for updating primal variables in the …
optimization problems. Practically, subproblems for updating primal variables in the …
Linear convergence rate of a class of distributed augmented lagrangian algorithms
We study distributed optimization where nodes cooperatively minimize the sum of their
individual, locally known, convex costs fi (x)'s; x ϵ ℝ d is global. Distributed augmented …
individual, locally known, convex costs fi (x)'s; x ϵ ℝ d is global. Distributed augmented …
A smooth primal-dual optimization framework for nonsmooth composite convex minimization
We propose a new and low per-iteration complexity first-order primal-dual optimization
framework for a convex optimization template with broad applications. Our analysis relies on …
framework for a convex optimization template with broad applications. Our analysis relies on …
Rate-improved inexact augmented Lagrangian method for constrained nonconvex optimization
First-order methods have been studied for nonlinear constrained optimization within the
framework of the augmented Lagrangian method (ALM) or penalty method. We propose an …
framework of the augmented Lagrangian method (ALM) or penalty method. We propose an …