Distributed Adaptive Optimization Algorithm for Fractional High-Order Multiagent Systems Based on Event-Triggered Strategy and Input Quantization

Abstract

This paper investigates the distributed optimization problem (DOP) for fractional high-order nonstrict-feedback multiagent systems (MASs) where each agent is multiple-input–multiple-output (MIMO) dynamic and contains uncertain dynamics. Based on the penalty-function method, the consensus constraint is eliminated and the global objective function is reconstructed. Different from the existing literatures, where the DOPs are addressed for linear MASs, this paper deals with the DOP through using radial basis function neural networks (RBFNNs) to approximate the unknown nonlinear functions for high-order MASs. To reduce transmitting and computational costs, event-triggered scheme and quantized control technology are combined to propose an adaptive backstepping neural network (NN) control protocol. By applying the Lyapunov stability theory, the optimal consensus error is proved to be bounded and all signals remain semi-global uniformly ultimately bounded. Simulation shows that all agents reach consensus and errors between agents’ outputs and the optimal solution is close to zero with low computational costs.

Quantization-Based Event-Triggered H∞ Consensus for Discrete-Time Markov Jump Fractional-Order Multiagent Systems with DoS Attacks

“…By integrating ETM and a quantized control technique, Ref. [33] addressed the distributed adaptive optimization problem of nonstrict feedback FOMASs with uncertainty. However, the general quantizers [30][31][32][33][34][35] are typically determined by fixed threshold parameters.…”

“…[33] addressed the distributed adaptive optimization problem of nonstrict feedback FOMASs with uncertainty. However, the general quantizers [30][31][32][33][34][35] are typically determined by fixed threshold parameters. This contributes to the decline in system performance.…”

“…A quantizer with dynamic switching features is more able to emphasize the robustness of the system, while traditional quantizers [30][31][32][33][34] lack the capability to achieve system stability through dynamic adjustments. 2.…”

Distributed optimization via active disturbance rejection control: A nabla fractional design

“…In addition, [27] studied the distributed optimization problem of fractional-order nonlinear uncertain multi-agent systems with unmeasured states, employing a neural network-based adaptive optimization control strategy. [28] addressed the distributed optimization problem of fractional-order non-strict-feedback multi-agent systems using neural networks and event-triggered schemes for optimization control. [18] investigated the fixed-time distributed time-varying optimization problem of nonlinear fractional-order multi-agent systems on unbalanced directed graphs, employing innovative distributed control methods.…”

Adaptive Neural Control for a Class of Random Fractional-Order Multi-Agent Systems with Markov Jump Parameters and Full State Constraints

“…All of the above studies are based on integer-order Markov jump MASs; actually, the adaptive control methods are also often applied to fractional-order systems to achieve a wide range of control objectives [34][35][36]. Introducing fractional-order Markov jump MASs and then developing an adaptive strategy for such systems has remained unaddressed so far.…”