With the increasing expansion of the power grid, economic dispatch problems have received considerable attention. A multi-agent coordinated deep reinforcement learning algorithm is proposed to deal with distributed nonconvex economic dispatch problems. In the algorithm, agents run independent reinforcement learning algorithms and update their local Q-functions with a newly defined joint reward. The double network structure is adopted to approximate the Q-function so that the offline trained model can be used online to provide recommended power outputs for time-varying demands in real-time. By introducing the reward network, the competition mechanism between the reward network and the target network is established to determine a progressively stable target value, which achieves coordination among agents and pledges the losses of the Q-networks to converge well. Theoretical analysis is given and case studies are conducted to prove the advantages compared with existing approaches.

This brief focuses on the distributed resource allocation problem (RAP) for a general linear heterogeneous multi-agent system (MAS), in which each agent adopts different structure parameters. By means of multi-agent consensus approach and symbolic-function-based fixed-time control theory, an initialization-free distributed resource allocation algorithm is designed. Moreover, the equality constraint is solved based on the proportional-integral (PI) control idea and the output of all agents tracks to the optimal solution within fixed-time. Finally, we reveal the effectiveness and fast convergence performance of our proposed algorithm by a simulation example.

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.

This paper focuses on the distributed resource allocation problem of a second-order multi-agent system with external disturbances. The global network resource equality constraint is considered. A distributed algorithm with an exponential convergence rate is proposed based on an internal model for a known bounded disturbance. By using the active disturbance approach to design a reduced-order extended state observer, the proposed algorithm is modified to reject an unknown external disturbance. Moreover, exponential convergence is achieved through the Lyapunov theory when the disturbance is constant. When the differential of the disturbance is bounded, by adjusting the control parameter of the observer, the proposed algorithm converges to an arbitrarily small neighbourhood of the optimal solution. Finally, our simulation results demonstrate the effectiveness of our proposed algorithm.

The rapid development of cyber-physical systems technique has promoted the development of autonomous systems. To perform the distributed task autonomously, this study delves into the problem of distributed resource allocation over second-order multiple autonomous physical systems. It aims to minimize the global cost function while allocating plenty of resources to finite distributed second-order agents. Since exogenous disturbances in the control channel are unavoidable, and the nonlinear dynamical system can more accurately describe the physical behavior of the system, i.e., Euler–Lagrange systems. The nonlinear term in the control channel is considered, including the exogenous disturbances and nonlinear dynamics. Unlike most existing results, the nonlinear term is assumed to be bounded by a known constant. To this end, the bounded nonlinear term is handled using a sign function-based discontinuous nonlinear control scheme. Based on this, a distributed method with an adjustable dual Lagrange multiplier is designed using the modified primal-dual scheme for achieving the optimal allocation result. In addition, its convergence proof is completed in view of the fixed-time stability and the set-valued LaSalle invariance principle. With our designed method, the agent state asymptotically converges to the exact solution for the undirected and weighted-balanced directed connected networks. Last, we adopt some case studies to certify the effectuality of the developed analytical outcomes.