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This paper presents the development of a controller for a fleet of unmanned aerial vehicles based on a distributed path planning strategy under a multiagent systems framework. The issue, treated as an online optimization problem, is solved using a particle swarm optimization (PSO) algorithm. The proposal was validated in experiments, considering different scenarios like fixed and mobile targets, external disturbances, and the loss of an agent. The proposed PSO is implemented independently in each vehicle in order to determine, by minimizing a cost function, the best paths that ensure the fleet formation control, target tracking, and collision avoidance. [ABSTRACT FROM AUTHOR]

This paper focuses on the resource allocation problem(RAP) with constraints under a fixed general directed topology by using the distributed sub-gradient algorithm with event-triggered scheme in multi-agent systems, where each agent owns a cost function and its state value is bounded. The distributed sub-gradient algorithm aims to minimise the total cost by a distributed manner while achieving an optimal solution. Unlike centralised methods, the triggering condition and algorithm for each agent are fully decentralised. At each instant of time, each agent updates its state by employing the states which are collected from itself and its neighbouring agents at their last triggering time. In order to illustrate the effectiveness of the proposed sub-gradient algorithm with event-triggered control law, one simulation example is presented before the conclusion. [ABSTRACT FROM AUTHOR]

With the development of big data and artificial intelligence, distributed optimization has emerged as an indispensable tool for solving large-scale problems. In particular, the multi-agent system based on distributed information processing can be elaborately designed for distributed optimization, in which the agents collaboratively minimize a global objective function made up of a sum of local objective cost functions subject to some local and/or global constraints. Inspired by the applications involving resource allocation, machine learning, power systems, sensor networks and cloud computing, a variety of distributed optimization models and algorithms have been investigated and developed. The optimization models include unconstrained and constrained problems in continuous and discontinuous systems with undirected and directed communication topology graphs. The constraints include bounded constraint, separable and inseparable equality and inequality constraints. Meanwhile, in distributed algorithms, every agent executes its local computation and updating on basis of its own data information and that exchanging with its neighboring agents by means of the underlying communication networks, in order to deal with the optimization problems in a distributed way. This paper is designed to provide a comprehensive overview of extant distributed models and algorithms for distributed optimization. [ABSTRACT FROM AUTHOR]

In this study, the authors propose a distributed discrete-time algorithm for unconstrained optimisation with event-triggered communication over weight-balanced directed networks. They consider a multi-agent system where each agent has a state and an auxiliary variable for the estimates of the optimal solution and the average gradient of the entire cost function. Agents send the states and auxiliary variables to their neighbours when their trigger errors exceed thresholds. They derive a convergence rate of the proposed algorithm which shows faster convergence to the optimal solution compared to the subgradient-based method. [ABSTRACT FROM AUTHOR]

Highlights • We propose a distributed primal-dual algorithm to solve constrained optimization problems over directed network. • The proposed algorithm adopts stochastic gradient descent method to update values of nodes for dealing with noise. • Theoretical analysis indicates that the algorithm can exactly seek the optimal solution to EDP with probability one. • The algorithm allows uncoordinated step-sizes for local optimization of each node. • To demonstrate the robustness and scalability of the proposed algorithm, we extend the algorithm to the network with communication delays. Abstract Economic dispatch problem (EDP) is one of the fundamental optimization problems in power systems, which involves a coupling linear constraint and several individual box constraints. In this paper, we propose a distributed stochastic gradient descent algorithm based on consensus theory to solve the EDP under directed network, where the convex cost function for each generator only needs to satisfy the condition that the function is strictly convex with Lipschitz continuous gradient. The proposed algorithm utilizes stochastic gradient descent to update values of generators for dealing with the noise which is incurred during the gradient estimation, and the step-sizes are heterogeneous. Under strictly convex assumption on objective functions, the algorithm can seek the exact optimal solution with probability one at the rate of O ln K K , where K is the number of iteration. Furthermore, the algorithm is also suitable and effective to the network with communication delays if the communication delays are bounded. Simulation results illustrate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

This paper considers the consensus problem in complex networks of uncertain discrete time agents. The coupling parameters among agents are locally self tuned by least-mean square ( LMS ) algorithm, without using any global information. In this process each agent minimizes a local cost function dependent on the error between the agent state and the average of neighbors states. Provided that the network graph is strongly connected, it is shown that for each agent the sequence of coupling parameters is convergent, and all agent states converge toward the same constant value. It is demonstrated that in the face of unknown high-frequency gain, the proposed algorithms generate such coupling parameters so that the overall multi-agent system is marginally stable with only one pole on the unit circle, located at λ = 1 . [ABSTRACT FROM AUTHOR]

In this study, we investigate the distributed convex optimisation problem of the multi-agent system over an undirected network, in which the global objective function is the sum of all the local cost functions and the local cost function of each agent is only known by itself. In order to save the computation and communication resources, the above optimisation problem is addressed by designing two zero-gradient-sum algorithms with state-based dynamic event-triggered mechanism, where the information communication only occurs at some discrete triggering time instants. The Zeno behaviour of the above event-triggered control scheme is excluded by the existence of the positive minimum inter-event time (MIET). The convergence is proved based on the Lyapunov method. Finally, we illustrate and evaluate the effectiveness of the proposed event-triggered algorithms through numerical experiments on simulated. [ABSTRACT FROM AUTHOR]

In this paper, two distributed multi-proximal primal–dual algorithms are proposed to deal with a class of distributed nonsmooth resource allocation problems. In these problems, the global cost function is the summation of local convex and nonsmooth cost functions, each of which consists of one twice differentiable function and multiple nonsmooth functions. Communication graphs of underlying multi-agent systems are directed and strongly connected but not necessarily weighted-balanced. The multi-proximal splitting is introduced to deal with the difficulty caused by the unproximable property of the summation of those nonsmooth functions. Moreover, it can also guarantee the Lipschitz continuity of proposed algorithms. Auxiliary variables in the multi-proximal splitting are designed to estimate subgradients of nonsmooth functions. Theoretically, the convergence analysis is conducted by employing Lyapunov stability theory and integral input-to-state stability (iISS) theory with respect to sets. It shows that proposed algorithms converge to the optimal point while satisfying resource allocation constraints. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a distributed receding horizon approach for multi-agent systems. First, the consensus problem with input constraints is considered. Second, the trajectory planning of multi-agent systems (MASs) evolving in environment with obstacles is studied, in which the information of obstacles can be obtained online once the obstacles are sensed by the agents within a limited sensing range. Based on receding horizon approach, a distributed control algorithm is developed to solve the two problems simultaneously. The algorithm can be given by solving the optimal control problem, in which the cost function is coupled with the dynamics of each interacting agent. The exchanged information between agents is the previous optimal control states of their neighbors at each update step. The system stability can be guaranteed with a terminal-state penalty in the cost function and a terminal-state region in the optimal control problem. Simulation studies are provided to verify the effectiveness of the proposed approach. [Copyright &y& Elsevier]

In this paper, the distributed fixed-time optimization problem is investigated for first-order multi-agent systems with strongly convex local cost functions. To solve this problem, a two-piece distributed fixed-time optimization algorithm is proposed. In the first piece, some local optimization controllers are designed for the agents such that each agent converges to its local cost function's minimizer in the unified fixed time under an arbitrary initial state. In the second piece, based on state information transmissions between neighboring agents and the local cost functions' Hessian matrices, some distributed optimization controllers are developed for the agents such that they converge to the global cost function's minimizer together in fixed time. Under the proposed algorithm, all the agents reach the global cost function's minimizer in fixed time. Moreover, this fixed settling time is independent of the agents' initial states and it can be predetermined according to the task demands. Numerical simulations demonstrate the effectiveness and advantages of the proposed distributed fixed-time optimization algorithm. [ABSTRACT FROM AUTHOR]