We propose two novel dynamic event-triggered control laws to solve the average consensus problem for first-order continuous-time multiagent systems over undirected graphs. Compared with most existing triggering laws, proposed involve internal variables, which play an essential role in guaranteeing that time sequence does not exhibit Zeno behavior. Moreover, some are special cases of ours. For self-triggered algorithm, continuous agent listening is avoided as each predicts its next and...

Based on the analysis of characteristics and operation status process industry, as well development global intelligent manufacturing a new mode for namely, deep integration industrial artificial intelligence Industrial Internet with is proposed. This paper analyzes existing three-tier structure which consists enterprise resource planning, execution system, control examines decision-making, control, management adopted by enterprises. this analysis, it then describes meaning an framework...

This paper considers distributed online optimization with time-varying coupled inequality constraints. The global objective function is composed of local convex cost and regularization functions the constraint sum functions. A primal-dual dynamic mirror descent algorithm proposed to solve this problem, where cost, regularization, are held privately revealed only after each time slot. Without assuming Slater's condition, we first derive regret violation bounds for show how they depend on...

This article investigates the distributed online optimization problem over a multi-agent network subject to local set constraints and coupled inequality constraints, which has lot of applications in many areas, such as wireless sensor networks, power systems, plug-in electric vehicles. In this problem, cost function at each time step is sum functions with them being gradually revealed its corresponding agent, meanwhile only are accessible agent. To address modified primal-dual algorithm,...

In this paper, we propose a fully distributed algorithm for second-order continuous-time multi-agent systems to solve the optimization problem. The global objective function is sum of private cost functions associated with individual agents and interaction between described by weighted undirected graph. We show exponential convergence proposed if underlying graph connected, each locally gradient-Lipschitz-continuous, restricted strongly convex respect minimizer. Moreover, reduce overall need...

Distributed bandit online convex optimization with time-varying coupled inequality constraints is considered, motivated by a repeated game between group of learners and an adversary. The attempt to minimize sequence global loss functions at the same time satisfy constraint functions, where are across distributed each round. sum local respectively, which adaptively generated revealed in manner, i.e., only values sampling instance, function held privately learner. Both one- two-point feedback...

This paper mainly investigates consensus problem with a pull-based event-triggered feedback control. For each agent, the diffusion coupling feedbacks are based on states of its in-neighbors at latest triggering time, and next time this agent is determined by in-neighbors' information. The general directed topologies, including irreducible reducible cases, investigated. scenario distributed continuous communication considered first. It proved that if network topology has spanning tree, then...

This article investigates distributed online convex optimization in the presence of an aggregative variable without any global/central coordinators over a multiagent network. In this problem, each individual agent is only able to access partial information time-varying global loss functions, thus requiring local exchanges between neighboring agents. Motivated by many applications reality, considered functions depend not on their own decision variables, but also variable, such as average all...

We propose two distributed dynamic triggering laws to solve the consensus problem for multi-agent systems with event-triggered protocol. Compared existing laws, proposed involve internal variables which play an essential role guarantee that time sequence does not exhibit Zeno behavior. Some are special cases of our laws. Under condition underlying graph is undirected and connected, it proven together protocol make state each agent converges exponentially average agents' initial states....

The distributed nonconvex optimization problem of minimizing a global cost function formed by sum <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> local functions using information exchange is considered. This an important component many machine learning techniques with data parallelism, such as deep and federated learning. We propose primal-dual stochastic gradient descent (SGD) algorithm, suitable for arbitrarily connected...

This article considers the distributed nonconvex optimization problem of minimizing a global cost function formed by sum local functions using information exchange. We first consider first-order primal–dual algorithm. show that it converges sublinearly to stationary point if each is smooth and linearly optimum under an additional condition satisfies Polyak–Łojasiewicz condition. weaker than strong convexity, which standard for proving linear convergence algorithms, minimizer not necessarily...

In this paper, we consider the distributed optimization problem, whose objective is to minimize global function, which sum of local convex functions, by using information exchange. To avoid continuous communication among agents, propose a algorithm with dynamic event-triggered mechanism. We show that scheme converges minimizer exponentially, if underlying graph undirected and connected. Moreover, free Zeno behavior. For particular case, also explicitly characterize lower bound for...

This paper considers the distributed online convex optimization problem with time-varying constraints over a network of agents. is sequential decision making two sequences arbitrarily varying loss and constraint functions. At each round, agent selects from set, then only portion function coordinate block at this round are privately revealed to agent. The goal minimize network-wide accumulated time. Two algorithms full-information bandit feedback proposed. Both dynamic static regret bounds...

Nonlinearities are present in all real applications. Two types of general nonlinear consensus protocols considered this paper, namely, the systems with communication and actuator constraints. The solutions understood sense Filippov to handle possible discontinuity controllers. For each case, we prove asymptotic stability minimal assumptions on nonlinearity, for both directed undirected graphs. These results extend literature more dynamics topologies. As applications established theorems,...

This paper presents the formulation and analysis of a fully distributed dynamic event-triggered communication based robust average consensus algorithm. Dynamic problem involves networked set agents estimating time-varying reference signals locally available to individual agents. We propose an asymptotically stable solution that is network disruptions. Since this algorithm requires continuous among agents, we introduce novel scheme reduce overall inter-agent communications. It shown free Zeno...

In this article, we consider distributed nonconvex optimization with the cost functions being over agents. Noting that information compression is a key tool to reduce heavy communication load for algorithms as agents iteratively communicate neighbors, propose three primal–dual compressed communication. The first two are applicable general class of compressors bounded relative error and third algorithm suitable classes absolute error. We show proposed have comparable convergence properties...