Do direct and inverse uncertainty assessment methods present the same results?

Hydrological models are simplified imitations of natural and man-made water systems, and because of this simplification, always deal with inherent uncertainty. To develop more rigorous modeling procedures and to provide more reliable results, it is inevitable to consider and estimate this uncertainty. Although there are different approaches...

An LMI approach to nonlinear state-feedback stability of uncertain time-delay systems in the presence of lipschitzian nonlinearities

This article proposes a new nonlinear state-feedback stability controller utilizing linear matrix inequality (LMI) for time-delay nonlinear systems in the presence of Lipschitz nonlinearities and subject to parametric uncertainties. Following the Lyapunov–Krasovskii stabilization scheme, the asymptotic stability criterion resulted in the LMI...

Train trajectory optimization for improved on-time arrival under parametric uncertainty

In this paper we study the problem of computing train trajectories in an uncertain environment in which the values of some system parameters are difficult to determine. Specifically, we consider uncertainty in traction force and train resistance, and their impact on travel time and energy consumption. Our ultimate goal is to be able to...

Safer reinforcement learning for robotics

Reinforcement learning is an active research area in the fields of artificial intelligence and machine learning, with applications in control. The most important feature of reinforcement learning is its ability to learn without prior knowledge about the system. However, in the real world, reinforcement learning actions may lead to serious damage...

Control of Switched Linear Systems: Adaptation and Robustness

As a special class of hybrid systems, switched systems have attracted a lot of attention in the last decade due to theoretical and practical interests. When controlling switched systems, a ubiquitous problem is the presence of large parametric uncertainties and external disturbances. However, the state of the art on adaptive and robust control...

Benchmarking model-free and model-based optimal control

Model-free reinforcement learning and nonlinear model predictive control are two different approaches for controlling a dynamic system in an optimal way according to a prescribed cost function. Reinforcement learning acquires a control policy through exploratory interaction with the system, while nonlinear model predictive control exploits an...

Efficient global robust optimization of unconstrained problems affected by parametric uncertainties

A novel technique for efficient global robust optimization of problems affected by parametric uncertainties is proposed. The method is especially relevant to problems that are based on expensive computer simulations. The globally robust optimal design is obtained by searching for the best worst-case cost, which involves a nested min-max...

Efficient uncertainty quantification in computational fluid dynamics

When modeling physical systems, several sources of uncertainty are present. For example, variability in boundary conditions like free stream velocity or ambient pressure are always present. Furthermore, uncertainties in geometry arise from production tolerances, wear or unknown deformations under loading. Uncertainties in computational fluid...