Optimization underpins many of the challenges that science and technology face on a daily
basis. Recent years have witnessed a major shift from traditional optimization paradigms …

Time-distributed optimization is an implementation strategy that can significantly reduce the
computational burden of model predictive control. When using this strategy, optimization …

We investigate tracking tasks for an automatic mobile robot with obstacle avoidance. To this
end we apply a linear model-predictive control (LMPC) method to the nonlinear robot model …

In this paper we focus on the solution of online problems with time-varying, linear equality
and inequality constraints. Our approach is to design a novel online algorithm by leveraging …

In this article, we propose a model-based approach to the design of online optimization
algorithms, with the goal of improving the tracking of the solution trajectory (trajectories) wrt …

In this paper, we propose control-theoretic methods as tools for the design of online
optimization algorithms that are able to address dynamic, noisy, and partially uncertain time …

Optimal control problems for nonsmooth dynamical systems governed by differential
variational inequalities (DVI) are called optimal control problems with equilibrium constraints …

Model predictive control (MPC) is of increasing interest in applications for constrained
control of multivariate systems. However, one of the major obstacles to its broader use is the …

In this work we adapt a prediction-correction algorithm for continuous time-varying convex
optimization problems to solve dynamic programs arising from Model Predictive Control. In …

This paper investigates a path-following method inspired by the semismooth $^* $ approach
for solving algebraic inclusions, with a primary emphasis on the role of uniform …