This paper proposes a novel optimization algorithm named PIDNAG, which innovatively incorporates the PID control strategy-comprising proportional, and time-dependent integral, and derivative components-into the Nesterov accelerated gradient method for solving convex optimization problems. From a control-theoretic perspective, we conduct rigorous theoretical analysis to demonstrate that the proposed method not only guarantees convergence but also significantly accelerates the optimization process. Extensive experimental results show that PIDNAG achieves remarkable convergence performance in various practical learning tasks, solidly validating its superior capability in convex optimization problems.

In the hierarchical control, a system could be monitored by high-level and low-level users, who may obtain different information even if both know the structure of the system. Low-level users can only observe the occurrence of a subset of events, while high-level users can observe the occurrence of all the events affecting the system dynamics. A system is said bisimulation strong non-deterministic non-interferent (BSNNI) if low-level users can neither infer the occurrence of high-level transitions, nor infer the nonoccurrence of high-level transitions. In this paper, we focus on BSNNI analysis and enforcement of bounded Petri nets. We show that, under the assumption of acyclicity of the high-level subnet, the notions of basis marking and basis reachability graph (BRG) allow to solve such problems with clear advantages in terms of computational complexity since they prevent exhaustive marking enumeration.

This paper deals with resilient and privacy-preserving control to optimize the daily operation costs of networked Battery Energy Storage Systems (BESS) in a multi-agent network vulnerable to various types of cyber-attacks. First, we formulate the optimization problem by defining the objective function and the local and coupling constraints. Next, we introduce a novel resilient decentralized control and optimization algorithm that can mitigate the effects of cyber-attacks, specifically false data injection attacks and hijacking, to enhance the network’s resilience. The proposed method is based on filtering out outlier Lagrange multipliers in a suitable dual problem. Our proposed algorithm has two main advantages compared to the existing literature. Firstly, it can solve problems where the coupling constraint is not restricted to the average or a function of the average of decision variables. Secondly, our algorithm extends the well-known dual decomposition and Lagrange multiplier method to the decentralized control problem of BESSs. In the proposed algorithm presented in this paper, only the data relevant to the dual problem is exchanged among the agents. Noticing that the data of the dual problem does not contain any private information, mitigating privacy concerns associated with our proposed algorithm. We formally prove the convergence of our algorithm to a feasible and sub-optimal solution. Additionally, simulations demonstrate the effectiveness of our results.Note to Practitioners—Optimal coordinated control of BESSs increases the power system’s reliability and reduces costs. With the expansion of the use of small-scale BESSs in household customers, it is possible to considerably increase the free capacity of power networks by optimally controlling these small BESSs. The methods published so far to solve such problems either share the private information of each BESS or are not resilient to failures or false data injection due to cyber-attacks. Therefore, these approaches are not favored in practical applications. Considering this practical motivation, in this paper, we present a decentralized algorithm to control a large set of BESSs in a platform vulnerable to various types of cyber-attacks without compromising privacy.

The federated learning (FL) paradigm aims to distribute the computational burden of the training process among several computation units, usually called agents or workers, while preserving private local training datasets. This is generally achieved by resorting to a server-worker architecture where agents iteratively update local models and communicate local parameters to a server that aggregates and returns them to the agents. However, the presence of adversarial agents, which may intentionally exchange malicious parameters or may have corrupted local datasets, can jeopardize the FL process. Therefore, we propose selective trimmed average (SETA), which is a resilient algorithm to cope with the undesirable effects of a number of misbehaving agents in the global model. SETA is based on properly filtering and combining the exchanged parameters. We mathematically prove that the proposed algorithm is resilient against data and local model poisoning attacks. Most resilient methods presented so far in the literature assume that a trusted server is in hand. In contrast, our algorithm works both in server-worker and shared memory architectures, where the latter excludes the necessity of a trusted server. The theoretical findings are corroborated through numerical results on MNIST dataset and on multiclass weather dataset (MWD).

This paper presents a data-driven experimental approach to identify models and design efficient automation strategies that optimize the drying-cooling process of 2D-shaped bread during the redesign of low-automated production systems. Thin-layer drying-cooling equations are shown to be suitable for describing the water and heat transfer dynamics in flatbreads. These equations can be used to predict the time required to achieve desired levels of moisture and temperature as a function of external temperature. This knowledge is then used to derive a discrete-time model of the drying-cooling process, which helps identify the most suitable re-engineering actions to improve production and quality while reducing waste. The case study involves a bakery that produces Carasau bread, which is a flat and dry bread typical of the Sardinian tradition with a long history and renewed interest in recent years. In this scenario, the drying-cooling process occurs during transportation via conveyor belts. We discuss how the process can be optimized using real-time measurements an adaptative control of the speed given the length of the conveyor belt. This represents a significant step forward in automating the manufacturing process of Carasau bread.

In this article, we study the dynamic resilient containment control problem for continuous-time multirobot systems (MRSs), i.e., the problem of designing a local interaction protocol that drives a set of robots, namely the followers, toward a region delimited by the positions of another set of robots, namely the leaders, under the presence of adversarial robots in the network. In our setting, all robots are anonymous, i.e., they do not recognize the identity or class of other robots. We consider as adversarial all those robots that intentionally or accidentally try to disrupt the objective of the MRS, e.g., robots that are being hijacked by a cyber–physical attack or have experienced a fault. Under specific topological conditions defined by the notion of(r,s)-robustness, our control strategy is proven to be successful in driving the followers toward the target region, namely a hypercube, in finite time. It is also proven that the followers cannot escape the moving containment area despite the persistent influence of anonymous adversarial robots. Numerical results with a team of 44 robots are provided to corroborate the theoretical findings.

This paper presents results on the convergence time of a previously proposed algorithm to solve the distributed task assignment problem on a network of agents. The considered algorithm consists in iterative local integer linear optimizations among the agents to cooperatively assign a set of tasks. Local optimizations are performed among a subset of randomly chosen neigh boring agents. This paper discusses a simulation study on the number of iterations and time required by the algorithm to find the best task assignment given the limited information available at each iteration. Simulations on large and small networks with local optimizations that involve a varying number of agents have been performed.

In this paper consensus in second-order multi-agent systems with a non-periodic sampled-data exchange among agents is investigated. The sampling is random with bounded inter-sampling intervals. It is assumed that each agent has exact knowledge of its own state at any time instant. The considered local interaction rule is PD-type. Sufficient conditions for stability of the consensus protocol to a time-invariant value are derived based on LMIs. Such conditions only require the knowledge of the connectivity of the graph modeling the network topology. Numerical simulations are presented to corroborate the theoretical results.

In this paper consensus in second-order multi-agent systems with a non-periodic sampled-data exchange among agents is investigated. The sampling is random with bounded inter-sampling intervals. It is assumed that each agent has exact knowledge of its own state at all times. The considered local interaction rule is PD-type. The characterization of the convergence properties exploits a Lyapunov-Krasovskii functional method, sufficient conditions for stability of the consensus protocol to a time-invariant value are derived. Numerical simulations are presented to corroborate the theoretical results.

In this study, the decentralized common reference frame estimation problem for multiagent systems in the absence of any common coordinate system is investigated. Each agent is deployed in a 2-D space and can only measure the relative distance of neighboring agents and the angle of their line of sight in its local reference frame; no relative attitude measurement is available. Only asynchronous and random pairwise communications are allowed between neighboring agents. The convergence properties of the proposed algorithm are characterized, and its sensitiveness against additive noise on the relative distance measurements is investigated. An experimental validation of the effectiveness of the proposed algorithm is provided.

We consider the scenario of a Wireless Sensor Networks (WSN) where the nodes are equipped with a programmable middleware that allows for quickly deploying different applications running on top of it so as to follow the changing ambient needs. We then address the problem of finding the optimal deployment of the target applications in terms of network lifetime. We approach the problem considering every possible decomposition of an application's sensing and computing operations into tasks to be assigned to each infrastructure component. The contribution of energy consumption due to the energy cost of each task is then considered into local cost functions in each node, allowing us to evaluate the viability of the deployment solution. The proposed algorithm is based on an iterative and asynchronous local optimization of the task allocations between neighboring nodes that increases the network lifetime. Simulation results show that our framework leads to considerable energy saving with respect to both sink-oriented and cluster-oriented deployment approaches, particularly for networks with high node densities and non-uniform energy consumption or initial battery charge.

In this paper a novel approach to the problem of decentralized agreement toward a common point in space in a multi-agent system is proposed. Our method allows the agents to agree on the relative location of the network centroid respect to themselves, on a common reference frame and therefore on a common heading. Using this information a global positioning system for the agents using only local measurements can be achieved. In the proposed scenario, an agent is able to sense the distance between itself and its neighbors and the direction in which it sees its neighbors with respect to its local reference frame. Furthermore only point-to-point asynchronous communications between neighboring agents are allowed thus achieving robustness against random communication failures. The proposed algorithms can be thought as general tools to locally retrieve global information usually not available to the agents.

In this paper the formation stabilization problem for a system of heterogeneous agents is considered. Agents are characterized by different linear dynamics, and assumed to be able to collaborate by exchanging information if they are within their range of communication. A sufficient algebraic condition for the stability of the formation based on a generalization of the Gerschgorin circle theorem for block matrices is proposed. Furthermore, conditions under which the formation remains stable under switching topology are investigated. Simulation results are given to corroborate the theoretical results.