扰动线性多智能体系统的分布式资源分配算法

For the distributed resource allocation problem over general heterogeneous linear multi-agent systems, this study designs a class of distributed optimization algorithms based on the dynamics of agents to suppress disturbances and simultaneously complete the optimal allocation of resources. This study designs an adaptive distributed optimization employing the state information of each agent for structure-known disturbances. In addition, the control parameter of the dual-variable Lagrange multiplier adaptively increases until consensus is achieved. Furthermore, the proposed algorithm is modified to be an output-based distributed optimization algorithm when the state information is not accessible. Based on LaSalle’s invariance principle, we show that the designed algorithms asymptotically converge to the optimal point when the global cost function is strongly convex. For unknown disturbances, this study proposes a distributed optimization algorithm based on an extended state observer. The proposed algorithm asymptotically converges to the theoretical optimal solution based on Lyapunov stability theory when the disturbance is constant, or its derivative tends to zero. Finally, the effectiveness of our proposed algorithms is illustrated by the provided simulation examples.