The control architecture for Connected and Automated Vehicles (CAVs) proposed in this work combines three features: Scalable, Distributed, and Reconfigurable. The termScalableis used in the sense that the number of CAVs can change in the runtime. The termDistributedis used in the sense that the control is totally embedded in the CAVs, without a central manager/coordinator. And the termReconfigurableis used in the sense that the CAVs may change their destinations and paths at any time. In this architecture, we assure the nonblocking and collision-free behavior of the system, and also the privacy of the path for each CAV. The control structure is composed of a local plant, a path controller, and a coordination controller, all modeled by automata. The dispute for road occupation between CAVs is handled to avoid collisions, based on a wireless communication where each CAV imposes disablings of events (actions) to other CAVs. We propose an online treatment for deadlock avoidance, with no need to compute a monolithic controller in any phase of the design. The path and coordination controllers are proposed to be embedded and synthesized in the CAV at runtime, which allows scalability and reconfiguration. The architecture is validated through a simulation environment in Robotarium and a practical experimental testbed with Lego Mindstorms robots. Two examples are given, as well as three simulations and three experiments are executed. One of the simulations was run for 54h, varying from 1 to 36 CAVs and the architecture is most efficient at 22.2% of the CAVs occupation rate on the map. Results show that all requirements were met.Note to Practitioners—This paper was motivated by the problem of controlling a large-scale system of CAVs, such as big cities. In these kinds of systems, vehicles are starting and stopping their routes at any time and it is desirable that the control system bescalable. Furthermore, it is unfeasible to implement on central controller/coordinator in such a large system, making it desirable to have adistributedcontrol system, embedded in each vehicle. Finally, due to the dynamics of traffic, such as congestion, it is desirable that the control system bereconfigurable. Existing control techniques have some of these features but not all of them in one methodology. In this paper, we propose one control architecture for CAVs, which comprises all three features at runtime. With these features, a CAV may, for example, start its route at any time (scaling the system) and change it at any time (reconfiguring) without interfering in the control of other CAVs. The proposed architecture supports many solutions for CAVs that can be modeled jointly, such as intersection management, road merging, path planning, and roundabouts. In the article, we develop the mathematics of the control and apply it to simulations and experiments. Results show that the proposed architecture can be applied in practice. Our codes for the simulations and experiments are shared in a public repository.

Relying only on behaviors that emerge from simple responsive controllers; swarms of robots have been shown capable of autonomously aggregate themselves or objects into clusters without any form of communication. We push these controllers to the limit, requiring robots to sort themselves or objects into different clusters. Based on a responsive controller that maps the current reading of a line-of-sight sensor to a pair of speeds for the robots' differential wheels, we demonstrate how multiple tasks instances can be accomplished by a robotic swarm. Using the dividing rectangles approach and physics simulation, a training step optimizes the parameters of the controller guided by a fitness function. We conducted a series of systematic trials in physics-based simulation and evaluate the performance in terms of dispersion and the ratio of clustered robots/objects. Across 20 trials where 30 robots cluster themselves into 3 groups, an average of 99.83% of them were correctly clustered into their group after 300 s. Across 50 trials where 15 robots cluster 30 objects into 3 groups, an average of 61.20%, 82.87%, and 97.73% of objects were correctly clustered into their group after 600 s, 900 s, and 1800 s, respectively. The object cluster behavior scales well while the aggregation does not, the latter due to the requirement of control tuning based on the number of robots.

The search for making industrial systems smarter is a challenge present in the Industry 4.0 scope. In this new industrial era, the automation processes operate in a more flexible and adaptable way, even if there is a system fault. However, depending on the level of complexity of the system operation, faut diagnosis and fault handling procedures can become difficult to evaluate and implement, especially when it is necessary to integrate them into the control system. Based on this challenging scenario, this article presents RAFAH, a reconfiguration architecture for fault handling in industrial systems. RAFAH is composed by three operational stages that autonomously compute flexible fault tolerant control strategies, combining formal methods for faults diagnosis in discrete event systems, supervisory control theory's extensions for modelling and designing control systems, and IEC 61499 resources for developing distributable and reconfigurable applications. The proposed methodology is validated by a case study in a fluid mixing process in which it is applied to diagnose faults and provide a new control strategy.

Supervisory Control Theory (SCT) provides a formal framework for controlling discrete event systems. It has recently been used to generate correct-by-construction controllers for swarm robotics systems. Current SCT frameworks are limited, as they support only (private) events that are observable within the same robot. In this paper, we propose an extended SCT framework that incorporates (public) events that are shared among robots. The extended framework allows to model formally the interactions among the robots. It is evaluated using a case study, where a group of mobile robots need to synchronise their movements in space and time-a requirement that is specified at the formal level. We validate our approach through experiments with groups of e-puck robots.

Dynamic reconfiguration enables automation and control systems to be reconfigured without the need to completely stop. With this feature, automation and control systems can adapt to new requirements as well as enable upgrades and maintenance quickly. This work proposes a methodology for the execution of dynamic reconfiguration in systems whose dynamics is driven by the occurrence of discrete events. Applying the theory of supervisory control, a methodology was developed to allow the change of the system control logic at runtime, ensuring the global and local consistency of the application. Next, the use of the methodology proposed in systems compliant with IEC 61499 standard is presented.

This paper deals with the implementation of supervisory control of discrete event systems into Programmable Logic Controllers (PLCs). It discusses the problems that arise in this type of implementation and presents an implementation methodology that solves these problems. The local modular approach is used to synthesize the supervisors and a case study problem is presented as an example.

This paper presents a new methodology for the implementation of discrete event-based control systems. An algorithm is proposed to transform an automata-based supervisor obtained from the supervisory control theory (SCT) in a Mealy finite state machine. This machine may be simplified in order to have a reduced number of state transitions. The machine simplification represents a formal method for a discrete event control system to be implemented in a controller. It is presented an example of a manufacturing system to illustrate such procedure. The implementation of the control for this system is performed in Ladder, a useful language for programmable logic controllers (PLCs). The methodology may be expanded to other programming languages and controller types as well.