Abstract
This paper presents a novel neuro-adaptive augmented distributed nonlinear dynamic inversion (N-DNDI) controller for consensus of nonlinear multi-agent systems in the presence of unknown external disturbance. N-DNDI is a blending of neural network and distributed nonlinear dynamic inversion (DNDI), a new consensus control technique that inherits the features of Nonlinear Dynamic Inversion (NDI) and is capable of handling the unknown external disturbance. The implementation of NDI based consensus control along with neural networks is unique in the context of multi-agent consensus. The mathematical details provided in this paper show the solid theoretical base, and simulation results prove the effectiveness of the proposed scheme.
Highlights
This paper presents a novel neuro-adaptive augmented distributed nonlinear dynamic inversion (N-DNDI) controller for consensus of nonlinear multi-agent systems in the presence of unknown external disturbance
We have presented a neuro-adaptive augmented distributed controller, which is designed based on Distributed Nonlinear Dynamic Inversion (DNDI)[13]
Before going to the main derivation of Neuro-adaptive DNDI, we present the philosophy of neuro-adaptive control design[25]
Summary
The problem definition is given. The objective is to design a neuro-adaptive consensus protocol that enables a class of nonlinear agents to achieve the consensus in the presence of external disturbance. Let us consider a group of N nonlinear agents. They are connected by the undirected and connected network topology. The dynamics of ith agent is given by Eqs. A brief overview of DNDI controller is presented here. The nominal dynamics of ith agent is given as follows. Ei denotes the consensus error of ith agent given by ei = diXi − aiX (9). Simplification of Eq (12) gives the expression of control Uid for ith agent as follows. Let us consider a smooth scalar function given by. Where,S ∈ RnN×nN is the left eigenvalue matrix of L ⊗ In , = diag{0, 2(L), 3(L), . N (L)} ⊗ In ∈ RnN×nN is eigenvalue matrix, ST S = SST = InN×nN. Remark 3 = S −1ST is positive definite matrix.
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