In the context of Industry 5.0, where environments are subject to unpredictable changes, achieving real-time adaptability and robust collision avoidance is essential for safe navigation and timely task execution. This paper introduces a Robust Grid-Based Receding Horizon Control scheme tailored for multi-robot logistics, designed to address dynamic obstacles and bounded multiplicative disturbances in unicycle-type robots. The method employs a single grid graph, continuously updated in real time using LiDAR data. A distributed robust set-theoretic model predictive control strategy leverages the grid graph to ensure safe and efficient navigation. The proposed approach is validated through realistic simulations in ROS/Gazebo, demonstrating its effectiveness in complex and dynamic scenarios.

This paper presents a control strategy for tracking trajectory and obstacle avoidance in constrained differential-drive robots operating in static but unknown environments. A robust receding horizon tracking controller is proposed, capable of handling state-dependent input constraints that arise when the robot’s dynamics are addressed through a feedback linearization technique. When a potential collision is detected along the reference trajectory, a switching law activates an obstacle avoidance mode. This mode leverages a vision module to build a grid map and applies an A* path planner to generate dynamically updated, obstacle-free waypoints that guide the robot around the obstacle. The same control scheme is used for both tracking and obstacle avoidance, ensuring consistency and efficiency. This framework aims to compute a collision-free, shortest, and safe path while adapting to environmental changes and satisfying velocity constraints. Once the obstacle is cleared, the robot seamlessly resumes the nominal tracking policy. The proposed method is validated using the digital twin Qbot2e differential-drive robot, achieving an average tracking RMSE of 0.06 m, maintaining a minimum obstacle clearance of 0.34 m, and avoiding all collisions across 100 runs.

Guaranteeing collision avoidance is of paramount importance in view of accomplishing missions where many agents are involved to share the same space. The main objective of this work is to expand the Turn-Based Command Governor approach in ([21]) by taking non-convexCollision Avoidanceconstraints into account when performing Plug-and-Play (PnP) operations among agents operating in a 2D environment. To deal with such a scenario, formal conditions that guarantee collision-free Plug-and-Play (PnP) operations are given. These conditions are based on the concept of safe areas, which define regions where agents can safely perform PnP operations without the risk of collision. The effectiveness of the proposed strategy is illustrated through various examples, highlighting its potential for mission accomplishment involving multiple agents.

Predictive Maintenance approaches are gaining popularity in the new Industry 4.0 paradigm as they offer superior benefits in terms of time and money savings when it is required to assess the current working capabilities of operating equipment to carefully schedule maintenance operations. This work deals with a control allocation strategy inspired by Model Predictive Control ideas and able to address the loss of effectiveness of actuating equipment arising from their continuous usage. The scheme here presented comprises two modules: a prognostic unit for monitoring the reliability conditions of the actuators and a re-configurable control allocation block that operates according to the deterioration degree of the present actuators. The benefits of the proposed approach are testified by the numerical simulations carried out on both an unstable system and a tanks network. In particular, it can be observed that the proposed method is capable of suggesting a time-window for maintenance interventions that prevents either stability or feasibility issues.Note to Practitioners—Nowadays, in the viewpoint of financial and technical issues in the industries, the predictive maintenance (PM) plays the main and important subject. In the variety of industries, such as oil, gas, petrochemical, power plant, or transmission and distribution infrastructures, which are using the actuators and pipelines, predictive maintenance (PM) is a critical topic to prevent the unwanted shutdown and unrequired maintenance costs. However, most PM research and solutions have paid attention mainly to the estimation of the Remaining Useful Life (RUL) of critical devices (i.e., pipelines) only. On the contrary, in this work we focus on the possibility of influencing the RUL by properly acting on the actuators’ effort. The proposed method consists in a dual-mode fault-tolerant control allocation technique developed for discrete-time systems subject to input saturation. The proposed predictive model-based control solution can be considered as a simple, structural and practical approach to minimize the maintenance cost and reliability by decreasing the number of maintenance interventions and optimizing the time for their repairing, by preventing or decreasing the probability of occurrence of unwanted failures events. The main limitation in this approach is that it assumes the RUL of the critical assets under investigation known. As a future research, this work will be extended to include a related RUL estimator hinging on online data.

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 addresses the problem of motion planning for an autonomous, tracked mobile robot whose mathematical model depends on uncertain parameters, with constrained moving capabilities, operating in a cluttered environment. The proposed algorithm exploits the concept of one-step ahead controllable sets. In particular, motion sequences compatible with uncertainties and nonlinear model dynamics are determined in the off-line phase with the aim to determine a collision-free path capable to accomplish the given mission. Conversely, the on-line operations are devoted to determine the most appropriate control action by solving a computationally simple optimization problem. Finally, some preliminary numerical results are instrumental to testify the effectiveness of the proposed approach.

In this paper, the resilient control problem for constrained cyber-physical systems subject to stealthy data intrusions on the communication channels is considered. The key idea consists in designing a neural network to act as the anomaly detector during the on-line operations. Accordingly the controller unit, based on model predictive control arguments, is developed to take advantage of the resulting detection capabilities. As its main merits are concerned, the overall control architecture has a two-fold merit with respect to the existing literature: it is avoided the need of modifying the detector structure whenever a different class of attacks is considered, and the occurrence of false positive events is significantly mitigated. Finally, a numerical example is provided to show the effectiveness and peculiarities of the proposed approach.

In this paper, an adaptive sensor selection architecture is developed to deal with distributed state estimation problems for multi-agent networked systems consisting of three different classes of nodes (plants, sensors and agents). Specifically, the problem of adequately fusing the sensors data coming from the plants and delivered to the agents, is addressed by evaluating their trustworthiness. This is achieved by exploiting a well-established approach in the power electronics: the Perturb&Observe algorithm that in the present framework allows one to select the more adequate group of sensors so as to compute at each time instant the best state estimate according to a given performance index. Some simulations are finally reported to testify the effectiveness of the proposed methodology.

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.

In this paper, a novel control architecture capable of managing denial-of-service attacks affecting the communication links between a group of interconnected systems and remote controllers is presented. The basic idea relies on the representation of the interconnected cyber-physical system as a leader-follower configuration so that adequate control actions are computed in order to isolate the attacked unit that otherwise could compromise system operations. Simulations on a multiarea power system confirm that the proposed control scheme can reconfigure the leader-follower structure in response to denial-of-service (DoS) attacks occurring on both sensor-to-controller and controller-to-actuator channels.

This paper evaluates the applicability of command governor (CG) strategies to the optimal management of drinking water supply systems (DWSSs) in both centralized and distributed ways. It will be shown that CG approaches provide an adequate framework for addressing the management of these large-scale interconnected systems in the presence of periodically time-varying disturbances (water demands) that can be anticipated by using time-series forecasting approaches. The proposed centralized and distributed CG schemes are presented, discussed, and compared when applied to the management of DWSS considering the same set of operational goals in all cases. This paper illustrates the effectiveness of all strategies using the Barcelona DWSS as a case study and highlighting the advantages of each approach.

High penetration of distributed generation (DG) in medium voltage (MV) power grids may easily lead to abrupt voltage raises in the presence of either low demand conditions or high power production from renewable sources. In order to cope with the possibly occurring voltage limit violation, the active power injected by the distributed generators is typically curtailed, being, however, such an approach suboptimal from an economical point of view and presenting several other disadvantages. To address this issue, the online management and coordination of the reactive power injected/absorbed by the distributed generators acting on the grid are proposed here. The approach is based on command governor ideas that are used here to optimally solve constrained voltage control problems in both centralized and distributed ways. The approach foresees an active coordination between some controllable devices of the grid, e.g., distributed generators and MV/high voltage transformers, in order to maintain relevant system variables within prescribed operative constraints in response to unexpected adverse conditions. Simulation results show that the proposed approach is more effective than approaches suggested by the current Italian norms on DGs connection.