In this paper, the problem of controlling autonomous vehicles in a Flexible Manufacturing System is addressed in order to optimize logistic operations. To this end, vehicles are required to navigate between machines and from/to the Load/Unload station. The core contribution of this paper is to propose a set-theoretic distributed Model Predictive Control in charge of controlling the autonomous vehicles properly integrated with a Reinforcement Learning scheme to address the routing problem. In addition, vehicles are organized as platoons in order to improve the efficiency of the overall architecture. The numerical simulation shows the effectiveness of the proposed approach.

In this paper, autonomous vehicles are considered for addressing logistic operations in manufacturing systems. The starting idea consists in organizing a given group of autonomous robots/vehicles in a finite set of platoons in charge to accomplish prescribed job(s) within the manufacturing system. Three aspects are then needed to be formally outlined: task scheduling, routing decisions and command inputs computations. Here, a new distributed multi-layer architecture has been conceived by using three methodologies: timed colored Petri nets, deep reinforcement learning and model predictive control. Roughly speaking, timed colored Petri nets are exploited to formally model the manufacturing system so that an optimal scheduling task complying with the required jobs and the available vehicles is derived; then, run-time routing decisions are obtained by using a distributed reinforcement learning algorithm which exploits the available information provided by the vehicle sensor module; finally, the distributed model predictive control algorithm is built by resorting to a set-theoretic approach where most of the computations are off-line performed. A flexible manufacturing system consisting of four machines and a Load/Unload station is used for simulation purposes. Specifically, five jobs are hypothesized and some scenarios with an increasing number of available vehicles are simulated. In order to evaluate the benefits of the proposed approach, a time criterion based on the completion of all the jobs is considered with the aim to put in light that increasing the number of vehicles improves the control performance until congestion phenomena become unavoidable. Note to Practitioners—This paper proposes an innovative methodology for addressing the logistic operations within flexible manufacturing systems (FMSs) by using a team of autonomous ground vehicles. Unlike existing approaches, the core of this framework consists in combining along a hierarchical structure the capabilities of timed colored Petri nets and the deep reinforcement learning techniques to determine a near-optimal scheduling and run-time routing decisions that are provided to the distributed model predictive units in charge to accomplish the prescribed task. This multi-layer architecture has two main merits: a single platoon, completely disconnected from the others, is devoted to perform its own job; computational burdens are affordable during the on-line operations because most of the computations are moved in the off-line phase.

In this article, we address a constrained regulation problem for networked control systems where the plants are modeled by polytopic linear descriptions, the state vector is partially available via output measurements, and the communication medium is unreliable. A control architecture is then proposed by considering a state-estimation-based robust model predictive control (MPC) strategy, designed to be resilient to regulation challenges while also preventing communication breakdowns when the convergence to the target is not practicable. Specifically, a deconvolution state observer is used for reconstruction purposes, and it is integrated with set-theoretic receding horizon principles to conceive a framework that meets both resilience and communication maintenance requirements.

This paper delves into the resilience challenges encountered by platoons of self-governing agents navigating city streets, particularly focusing on the impact of misleading data infiltrating neighborly communication channels. The core issue centers around defining the trajectory paths of vehicles through routing decisions that must adhere to traffic fl ow constraints. To address this, we employ a decentralized model predictive control (MPC) strategy, leveraging deep reinforcement learning (DRL) abilities for effective decision-making in managing the regulation tasks. Additionally, the paper explores cybersecurity concerns by developing an effective anomaly detection system and practical attack countermeasures to enhance the resilience of the vehicle platoons against data tampering and other cyber threats. Through simulations and experiments, we demonstrate the efficacy of our approach in maintaining smooth traffic flow and ensuring secure communication within autonomous vehicle networks.

In this paper, we present a novel resilient control architecture capable to manage replay attacks for multi-agent discrete-time linear systems subject to input and state constraints. By considering a leader-follower configuration, the basic idea consists of exploiting model predictive control arguments to apply adequate control action in order to isolate the attacked unit that otherwise could compromise system operations. To this end, a set-theoretic receding horizon control strategy is developed that is also capable to instantaneously detect the attacked agent along the platoon chain. Finally, we describe a set of simulations on a group of mobile robots to demonstrate the effectiveness of the proposed approach.