Data-Driven Multiagent Systems Consensus Tracking Using Model Free Adaptive Control

Abstract

This paper investigates the data-driven consensus tracking problem for multiagent systems with both fixed communication topology and switching topology by utilizing a distributed model free adaptive control (MFAC) method. Here, agent's dynamics are described by unknown nonlinear systems and only a subset of followers can access the desired trajectory. The dynamical linearization technique is applied to each agent based on the pseudo partial derivative, and then, a distributed MFAC algorithm is proposed to ensure that all agents can track the desired trajectory. It is shown that the consensus error can be reduced for both time invariable and time varying desired trajectories. The main feature of this design is that consensus tracking can be achieved using only input-output data of each agent. The effectiveness of the proposed design is verified by simulation examples.

Data Driven Distributed Bipartite Consensus Tracking for Nonlinear Multiagent Systems via Iterative Learning Control

“…Remark 1: The above assumptions are there fundamental assumptions of DBCILC approach and the reasonability of them have been discussed in [8], [23] and [38].…”

“…It is noted that most of the schemes mentioned above need to establish neural networks to design controllers, which makes preparing the external testing signals and training processes inescapable. Recently, some useful results have been reported for unknown multiagent systems, such as Model-Free Adaptive Control (MFAC) [23]- [24], Q-Learning [25]- [27], Iterative Feedback Tuning (IFT) [28]- [29], Simultaneous Perturbation Stochastic Approximation (SPSA) [30]- [31], Iterative Learning Control (ILC) [32]- [38], Virtual Reference Feedback Tuning (VRFT) [39].…”

“…Generally, this paper is inspected by [14], [23], and [34]. The heterogeneity of MASs is considered in [14] to realize BC, however, the controller is complex.…”

Improved Model-Free Adaptive Sliding-Mode-Constrained Control for Linear Induction Motor considering End Effects

“…The main design steps of the MFAC are divided into three categories: (1) using CFDL technique to transfer the nonlinear system into self-designed linear model based on a parameter called pseudo-partial-derivative (PPD), (2) estimating the value of the PPD through a variety of methods, and (3) devising the controller based on self-designed linear model. For now, MFAC has been widely applied in all kinds of fields, such as multiagent systems [20], chemical process [19,21], and intelligent transportation [22]. Moreover, due to the fact that sliding-mode control (SMC) is designed without object parameters and disturbance, it gets the merits of quick response and high fitness.…”

Adaptive-Observer-Based Data Driven Voltage Control in Islanded-Mode of Distributed Energy Resource Systems

“…In [23], an improved MFAC algorithm was proposed for the coordination design of a wide-area power system stabiliser (WAPSS) to ensure the cooperation of the controllers under different operating conditions without tedious system modelling. In [24], the authors proposed a distributed MFAC scheme for multi-agent systems to solve consensus tracking problems only by using the input/output (I/O) data of each agent. Theoretical analysis, simulation, and practical application proved that the MFAC method has the advantages of strong robustness, small computational burden and easy implementation [19][20][21]25,26].In this paper, we propose a novel data-driven MFAC method based on the multiple adaptive observer technique to accommodate the multi-input multi-output (MIMO) DER system.…”