Distributed Neuro-Adaptive Prescribed Performance Consensus Control for Nontriangular Structural Multiagent Systems

For a class of nontriangular structural multiagent systems, this article presents a neuro-adaptive prescribed performance consensus control scheme. By using mean value theorem to isolate the virtual variables and neural networks to approximate the ideal controller, the system model is reconstructed, based on which the virtual controllers are able to be derived. The algebraic-loop problem is circumvented utilizing the properties of basis function. With the proposed performance functions, an error transformation is presented, based on which the controller scheme is developed. It is ensured that the follower agents synchronize at a predefined speed, and synchronization error converges to a specified range within a given time. Two Simulations demonstrate the effectiveness of the presented control method.