{Reference Type}: Journal Article
{Title}: Improved disturbance observer-based fixed-time adaptive neural network consensus tracking for nonlinear multi-agent systems.
{Author}: Zhang N;Xia J;Park JH;Zhang J;Shen H;
{Journal}: Neural Netw
{Volume}: 162
{Issue}: 0
{Year}: May 2023 17
{Factor}: 9.657
{DOI}: 10.1016/j.neunet.2023.03.016
{Abstract}: This paper is concerned with the problem of fixed-time consensus tracking for a class of nonlinear multi-agent systems subject to unknown disturbances. Firstly, a modified fixed-time disturbance observer is devised to estimate the unknown mismatched disturbance. Secondly, a distributed fixed-time neural network control protocol is designed, in which neural network is employed to approximate the uncertain nonlinear function. Simultaneously, the technique of command filter is applied to fixed-time control, which circumvents the "explosion of complexity" problem. Under the proposed control strategy, all agents are enable to track the desired trajectory in fixed-time, and the consensus tracking error and disturbance estimation error converge to an arbitrarily small neighborhood of the origin, meanwhile, all signals in the closed-loop system remain bounded. Finally, a simulation example is provided to validate the effectiveness of the presented design method.