Fully distributed coordination learning control of second‐order nonlinear multi‐agent systems with input saturation

Abstract

This paper develops the coordination learning control of second-order non-linear multi-agent systems with input saturation. Fully distributed learning consensus protocols with fully saturated parameter adaptive updating laws are designed. Although there exists input saturation in the dynamics of each follower agent, the global perfect consensus tracking can be realized over a finite time interval. Meanwhile, an extension to formation control has been made, and two illustrative examples certify the validness of the presented algorithms.

Iterative Learning Tracking Control of Nonlinear Multiagent Systems with Input Saturation

“…Lemma 1 (see [31]). If graph G is connected, then the symmetric matrix H associated with G is positive definite.…”

“…e study in [30] continued to improve the semiglobal multiagent control of input saturation by using low-gain and inverse saturation methods based on literature [28]. e study in [31] used Lyapunov theory to design an adaptive iterative learning algorithm with time-varying coupled-gain full-saturation parameter update to solve the problem of multiagent coordinated learning control with input saturation. However, most scholars have rarely investigated the relationship between input saturation and nonlinearity.…”

“…e design of control protocol will be further complicated due to the introduction of nonlinear dynamics. (ii) In comparison with [31,32], the present work discusses an adaptive control protocol with iterative learning control for nonlinear multiagent systems. Moreover, the adaptive updating laws for time-varying parameters are designed.…”

Distributed Coordination for a Class of High‐Order Multiagent Systems Subject to Actuator Saturations by Iterative Learning Control

“…Consequently, it is useful to take into account actuator saturations in system analysis. Recently, a large number of work about MASs with actuator saturations emerge [25][26][27][28][29][30][31][32]. References [25][26][27][28][29] studied consensus algorithms under one-dimensional system framework when time goes to infinity and [30][31][32] tackled the consensus problem by iterative learning control (ILC).…”

“…Recently, a large number of work about MASs with actuator saturations emerge [25][26][27][28][29][30][31][32]. References [25][26][27][28][29] studied consensus algorithms under one-dimensional system framework when time goes to infinity and [30][31][32] tackled the consensus problem by iterative learning control (ILC). In [30], authors addressed the neural network consensus for the second-order MASs subject to saturation input; authors in [31] proposed the fully distributed learning scheme and made use of the properties of saturation function to solve actuator saturations; the leaderfollower consensus for the first-order MASs with input saturation was researched in [32].…”