IpOpt/AMPL code sample
Download Code related to the IpOpt and AMPL use to solve time optimal control problems.
Developed by Jérôme Lohéac, Emmanuel Trélat & Enrique Zuazua
Optimal control applied to collective behaviour
The standard approach for solving a driving problem is a leadership strategy, based on the attraction that a driver agent exerts on other agent. Repulsion forces are mostly used for collision avoidance, defending a target or describing the need for personal space. We present a “guidance by repulsion” model describing the behaviour of two agents, a driver and an evader...
Greedy Control
Control of a parameter dependent system in a robust manner. Fix a control time $T > 0$, an arbitrary initial data $x^0$, and a final target $x^1 \in R^N$...
From finite to infinite-dimensional models (FI)
Our team has made several contributions in the description of the limit behaviour, as the mesh sizes tend to zero, of numerical schemes for wave and Schrödinger equations from a…
Models involving memory terms & hybrid PDE+ODE systems (MHM)
Control theory for PDEs has been quite exhaustively developed for model problems (heat and wave equations). But other important models in applications, of hybrid nature, remain poorly understood. This is…
Inverse design and control in the presence of singularities (SINV)
Some important PDE models in Continuum Physics, such as hyperbolic conservation laws, represent a major challenge from a control viewpoint for two (closely related) reasons: solutions lack regularity properties and…
Control under constraints (CC)
Most of the existing theory of controllability for PDEs has been developed in the absence of constraints on the controls and states. Thus, in practice, available results do not guarantee…
Long time horizon control (LTHC)
Control problems for evolution PDEs are most often considered in finite time intervals, without paying attention to the length of the control horizon and how it affects the nature of…
Control of parameter dependent problems (PDC)
In real applications, models are not completely known since relevant parameters (deterministic or stochastic) are subject to uncertainty and indetermination. Accordingly, for practical purposes, robust analytical and computational methods are…