Consensus Optimization Control of Nonlinear High-Order Multiagent Systems under External Disturbances

This paper investigates the consensus optimization control problem for high-order nonlinear multiagent systems (MASs) under external disturbances. The consensus optimization control of high-order nonlinear systems is made more difficult by the presence of external disturbances in real-world engineering applications. Thus, a double closed-loop optimization control scheme is proposed in this paper. In the optimization loop, optimal reference signals are generated using local information. The control loop employs a radial basis function neural network (RBFNN) to approximate unknown nonlinear functions and an adaptive law to estimate the upper bound of external disturbances. Based on this, a tracking controller is designed to follow the optimal reference signals. Consequently, the goal of achieving output consensus among MASs is realized while optimizing the global cost function. The effectiveness of the proposed control scheme is verified through simulations.