Josep Olm Miras

Automatic Guided Vehicles (AGVs) are widely used in flexible manufacturing systems for material handling inside the factory. Traffic management strategies, required to guarantee a conflict-free operation of the overall fleet, discretize the workspace of the AGVs and use the resulting graph model for route planning and execution. In zone control approaches, AGVs move from node to node on a permit basis, with limitations on the allowed number of AGVs at a time in each area of the graph to prevent and/or resolve deadlocks and conflicts. Hence, for an optimal implementation of traffic controllers in real manufacturing systems, it is essential to understand how the layout discretization influences the performance of the AGV network. This paper analyzes its effect in grid-like shaped workspaces by using a representative zone control algorithm and a recently developed improvement of it. Realistic numerical experiments on different layouts reveal that denser discretizations do not yield faster executions or increase in throughput, while lower control periods in the permit system entail significant performance uplifts.

Automated guided vehicles (AGVs) are widely used for material handling in warehouses and automated production lines due to their high efficiency and low failure rate with respect to human operated load carriers. However, AGVs usually interact with each other because of the restricted capacity of the layout, and conflicts arise. Although many traffic scheduling algorithms have been proposed to address the AGV fleet control problem, most of them are inefficient for collision and deadlock avoidance in dynamic environments. This paper proposes an improved dynamic resource reservation (IDRR) based method which renders time-efficient task completion and deadlock-free movements of multiple AGVs in a manufacturing system. Unlike traditional approaches, most of which adopt a dynamic single agent reservation of the shared resource points and/or force path deviations, IDRR exploits dynamic multiple reservations of shared resource points. This is combined with a conflict detection and resolution method that accommodates the AGV motions when they meet at a resource point. Extensive, realistic simulation results demonstrate the feasibility and efficiency of the proposed collision and deadlock prevention method in productivity, travelled distance, and time completion of the assigned tasks. The proposal can be implemented on both central and local controllers.

Synchronization is a typical dynamical behaviour of interconnected systems that is being extensively studied in neural networks. However, most of the research considers real-valued neural networks, and less results have been obtained on their complex-valued counterparts. This work presents two sliding mode control strategies to achieve synchronization in a complex-valued neural network. The former simplifies an already existing technique that splits the control into real and imaginary parts. The latter extends a fully complex-valued sliding approach for generic complex-valued dynamical systems to the multi-input multi-output case, and shows its efficiency and higher performance in terms of finite reaching time in the synchronization of complex-valued neural networks. The approach is validated via numerical simulations.

The deployment and coordination of mobile sensor networks for coverage control applications can present several practical challenges, including how to efficiently share limited communication resources and how to reduce the use of localization devices (e.g., radars and lidars). One potential solution to these challenges is to reduce the frequency at which agents communicate or sample each other's position. In this article, we present a distributed asynchronous self-triggered control policy for centroidal Voronoi coverage control that is shown to decrease the sampling or communication instants among agents without degrading the performance of the mobile sensor network. Each agent independently decides when to sample the position of nearby agents and uses outdated information of its neighbors until new information is required. We prove that the locational cost function describing the distribution of agents monotonically decreases everywhere outside of a bounded neighborhood around the group's optimal configuration and that the agents asymptotically converge to their Voronoi centroids if the data-sampled centroid errors approach zero. In addition, we show that the sampling intervals are always positive and lower bounded and, as illustrated by simulations and experiments, they tend to stabilize at a large value as the mobile sensor network comes to a steady state. Simulations and experiments with ground vehicles validate the control strategy and show that the proposed policy can achieve similar level of performance as a continuous or fast periodic implementation.

When an oscillator switches abruptly between different frequencies, there is some ambiguity in deciding how the system should be modelled at the switch. Here we describe two seemingly natural models of a switch in a simple periodically-forced harmonic oscillator, which disagree starkly in their predictions of its long time behaviour. Attempting to resolve the disagreement by `regularizing' the switch not only preserves the disagreement, but shows it increases with time. One of the models corresponds to a conventional `Filippov' description of a nonsmooth system, while the second exhibits a structure that irreversibly ages, developing a number of novel multi-scale behaviours that we believe have not been reported before. These include slow-fast staircases, novel mixed-mode oscillations, and a synchronized canard explosion. These features are proven to exist using asymptotic analysis, but as they involve a slow-fast time-scale separation that increases with time, they lie beyond the reach of numerical methods.

This work presents a nonlinear control strategy for a magnetically coupled multiport dc-dc converter aimed at automotive applications. First, a behavioural dynamic model, based on averaging the power flowing among the ports, is derived. Then, a control algorithm, based on feedback linearization techniques, is proposed. Essentially, the controller ensures decoupling and a linear dynamic behaviour thanks to a transformation of control and state variables, and a conventional PI action is devised to render asymptotic stability, as well as robustness to load uncertainties. The inversion of the control transformation, which involves nonlinear terms, is carried out using linear approximations with no significant performance decay. Hence, simplicity and efficiency are the main features of the control design. The proposed controller is validated via experimental results.