Milad Shahvali

TL;DR: In this article, the problem of distributed adaptive neural control is addressed for a class of uncertain non-affine nonlinear multi-agent systems with unknown control directions under switching directed topologies.

TL;DR: A novel fully distributed controller is developed based on backstepping technique and neuro-adaptive update mechanism to ensure bipartite consensus of multiple fractional-order nonlinear systems with output constraints and it is shown that all the closed-loop error signals are uniformly ultimately bounded.

TL;DR: Finite-time bipartite synchronization of multi-agent systems is assessed here and a virtual affine variable is introduced, and neural network along with minimal learning parameter principle are employed to approximate composite uncertainties including unknown functions in the system dynamics, unknown control coefficients and control inputs.

TL;DR: In this article, a distributed output-feedback control is proposed to synchronize a network of Euler-Lagrangian (EL) systems, which possesses nonlinear uncertainties, unmeasured states, and stochastic disturbances.

TL;DR: In this article, a distributed containment output-feedback control approach for a general class of stochastic uncertain nonlinear multi-agent systems is considered, where local linear state observers are designed to deal with the unmeasured states.