Abstract
In this paper, the consensus problem is addressed for multi-agent systems. The dynamics of each agent contain unknown uncertain/nonlinear terms and unknown time delays. A type-3 fuzzy logic system is developed to tackle the effect of unknown dynamics and design a hybrid controller. The policy scheme involves two control signals for the stabilization of the approximation and consensus error of each agent dynamic. To this end, based on the concept of the model predictive control approach, the constrained control laws are designed and updated at each time step. The simulations results portray the error signals. Feasibility, appropriate convergence, and proper transient response are the main merits of the suggested method.
Highlights
Based on the linear matrix inequality (LMI), deep T2-fuzzy logic system (FLS), and restricted Boltzmann machine (RMB) schemes, the leader following of fractional-order Multi-agent systems (MASs) in the presence of unknown dynamics was studied in [26]
Based on a compact form of the agent dynamics, the consensus problem of MASs with time delays, heterogeneous structures, unknown dynamic models, and nonlinear terms is analyzed via a novel distributed learning-based control policy
A Duffing–Holmes chaotic system and a nonlinear heterogeneous MASs are considered to evaluate the effectiveness of the suggested control protocol
Summary
Multi-agent systems (MASs) have been the focus of a variety of research papers because of their extensive applications in diverse fields [1]. In the case of heterogeneous discrete-time MASs, the output synchronization issue was investigated via an adaptive distributed observer [6]. A distributed resilient controller was designed to tackle sensor faults/attacks and study the ultimate boundedness of heterogeneous MASs [9]. Based on the linear matrix inequality (LMI), deep T2-FLS, and restricted Boltzmann machine (RMB) schemes, the leader following of fractional-order MASs in the presence of unknown dynamics was studied in [26]. A distributed FLS-based control technique was suggested for the output consensus of heterogeneous stochastic MASs [27]. Utilizing FLSs and a distributed fault-tolerant protocol, a consensus of nonlinear heterogeneous-switched MASs was studied in [29]. Based on a neuro-fuzzy control scheme and a high-gain observer, the synchronization of heterogeneous MASs was investigated in [30]. Based on a polynomial fuzzy modeling approach, the consensus of MASs under switching topologies was ensured in [31]
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