Adaptive neural-network-based distributed fault estimation for heterogeneous multi-agent systems

This contribution addresses the issue of distributed fault estimation for heterogeneous multi-agent systems which are composed of unmanned ground vehicles and unmanned aerial vehicles in the presence of actuator faults, completely unknown nonlinearities and external disturbances. Given that these two types of agents have different state dimensions and the motion of unmanned aerial vehicles in the X OY plane and Z-axis is relatively independent, the heterogeneous multi-agent systems can be divided into the X OY plane of all agents' position subsystem and the Z-axis of unmanned aerial vehicles' position subsystem. Then, combining the influences of completely unknown nonlinearities and external disturbances, an adaptive neural-network-based distributed fault estimation scheme is proposed to effectively estimate unknown actuation effectiveness parameters and can be applied to X OY plane and Z-axis of heterogeneous multi-agent systems separately. During the design of the observer, the neural network methodology is adopted to approximate completely unknown nonlinearities and a proper adaptive up-date law to estimate the 2-norm upper bound of disturbances and compensate for the influences of disturbances is designed. With output from a local agent and its neighbors, the proposed observer can be built on this agent, realizing simultaneous estimation of possible faults occurring in both the selected agent and its neighbor agents, which presents a new distributed framework. At last, simulation results are shown to illustrate the feasibility and effectiveness of the presented fault estimation algorithm. (c) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.