Leader‐Following Consensus for Second‐Order Nonlinear Multiagent Systems with Input Saturation via Distributed Adaptive Neural Network Iterative Learning Control

Abstract

In this paper, the consensus tracking control problem of leader-following nonlinear multiagent systems with iterative learning control is investigated. The model of each following agent consists of second-order unknown nonlinear dynamics and the external disturbance. Moreover, the input of each following agent is subject to saturation constraint. It is assumed that the information of leader is not available to any following agents, and the radial basis function neural network is introduced to approximate the nonlinear dynamics. Then, a distributed adaptive neural network iterative learning control protocol and the adaptive updating laws for the time-varying parameters are proposed, respectively. A new Lyapunov function is constructed to analyze the validity of the presented control protocol. Finally, a numerical example is provided to verify the effectiveness of theoretical results.

Iterative Learning Tracking Control of Nonlinear Multiagent Systems with Input Saturation

“…A comparative simulation result is presented to further clarify the effectiveness of the design method. Considering the designed control protocol of this paper (Scheme 1), the control law of [7] (Scheme 2) and the control law without input saturation (Scheme 3) are presented. e simulation results are shown in Figure 5.…”

“…In recent years, multiagent control has achieved great breakthroughs due to the rapid development of coordinated control of dynamic systems. Multiagent systems have a wide range of applications in many engineering fields, such as formation control of various robots, positioning of distributed sensor networks, and multisatellite collaborative control [5][6][7][8][9][10].…”

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).…”

Iterative Learning Consensus Control for Nonlinear Partial Difference Multiagent Systems with Time Delay

“…In recent years, MASs's cooperative control has been widely used in the industry, such as formation control for drones [3], satellite attitude control [4], and congestion control of communication networks [5]. At present, the main problems are collaborative control of MASs cluster control [6], formation control [7], and consensus control [8]. Among them, the consensus problem of MASs is its most fundamental problem.…”