Statistically Distinct Plans for Multi-Objective Task Assignment

We study the problem of finding statistically distinct plans for stochastic task assignment problems such as online multi-robot pickup and delivery (MRPD) when facing multiple competing objectives. In many real-world settings robot fleets do not only need to fulfil delivery requests, but also have to consider auxiliary objectives such as...

Discrete-Time Fractional-Order Dynamical Networks Minimum-Energy State Estimation

Fractional-order dynamical networks are increasingly being used to model and describe processes demonstrating long-term memory or complex interlaced dependencies among the spatial and temporal components of a wide variety of dynamical networks. Notable examples include networked control systems or neurophysiological networks which are created...

Learning for Control: An Inverse Optimization Approach

We present a learning method to learn the mapping from an input space to an action space, which is particularly suitable when the action is an optimal decision with respect to a certain unknown cost function. We use an inverse optimization approach to retrieve the cost function by introducing a new loss function and a new hypothesis class of...

Elastic FemtoCaching: Scale, Cache, and Route

The advent of elastic Content Delivery Networks (CDNs) enable Content Providers (CPs) to lease cache capacity on demand and at different cloud and edge locations in order to enhance the quality of their services. This article addresses key challenges in this context, namely how to invest an available budget in cache space in order to match...

A flow-based integer programming approach to design an interurban shared automated vehicle system and assess its financial viability

Shared automated vehicles (SAV) are expected to become part of on-demand transport systems in the future. In an interurban context, SAV systems may potentially contribute to significantly improve the availability and quality of public transport. In this paper, we propose a flow-based integer programming approach to design and assess the...

Fault isolation for large scale discrete-time systems based on implicit set representation

To detect faults in a system we can adopt an observer, designed for the healthy system, and monitor the discrepancy between actual and expected behaviour of the residual (difference between the system output and its estimate). To isolate faults, we can compute the invariant sets associated with each fault, and their projection in the residual...

Model Predictive Control for continuous piecewise affine systems using optimistic optimization

This paper considers model predictive control for continuous piecewise affine (PWA) systems. In general, this leads to a nonlinear, nonconvex optimization problem. We introduce an approach based on optimistic optimization to solve the resulting optimization problem. Optimistic optimization is based on recursive partitioning of the feasible set...

Receding-horizon control for max-plus linear systems with discrete actions using optimistic planning

This paper addresses the infinite-horizon optimal control problem for max-plus linear systems where the considered objective function is a sum of discounted stage costs over an infinite horizon. The minimization problem of the cost function is equivalently transformed into a maximization problem of a reward function. The resulting optimal...

Using dedicated EV charging areas to resolve grid violations caused by renewable energy generation

EV Charging is expected to be a significant load on the grid in the future. But they can also be a part of the solution to grid violations. As the amount of renewable energy generation increases, there is a large amount of power flowing in the reverse direction in the distribution network, which is undesirable. To reduce this, EVs can charge at...