Abstract
In this paper, a discussion is made on the consensus tracking control by iterative learning method for high-order nonlinear multi-agent systems. Among them, all agents with initial state errors are enabled to perform a given repetitive task over a finite interval. The method proposed can achieve consensus tracking through a series of initial shifts correction actions. In the process of tracking, this algorithm rectifies the initial error of the state x n of each agent at first, then the error of x n-1 , and so on. All of these rectifying actions are finished in a specified interval. Furthermore, the algorithm has shown effective in the improvement of tracking performance through simulation.
Highlights
As a fundamental problem for the distributed control of multi-agent systems (MASs), the consensus problem is the leader-follower formulation, which is designed to ensure that multi-agents neighboring individuals in the systems collectively achieve the same goal through communication protocols.In recent years, more and more scholars have focused on the cooperative tracking problem of multi-agent systems due to its wide application, such as drone control, robot formation and distributed neural network control [1]–[4]
At the beginning of this century, some achievements have been made in solving the consensus problem
Li et al.: iterative learning control (ILC) for Nonlinear MASs With Initial Shifts multi-agent systems in the directed network that didn’t meet the global Lipschitz condition, if the range of initial shifts variation gradually narrows, [41] proposed a robust cooperative learning strategy to carry out the high-precision formation
Summary
As a fundamental problem for the distributed control of multi-agent systems (MASs), the consensus problem is the leader-follower formulation, which is designed to ensure that multi-agents neighboring individuals in the systems collectively achieve the same goal through communication protocols.In recent years, more and more scholars have focused on the cooperative tracking problem of multi-agent systems due to its wide application, such as drone control, robot formation and distributed neural network control [1]–[4]. Researches on neural networks have rapidly increased They are used to achieve the consensus tracking of nonlinear multi-agent systems in [16] and [17]. Focused on first-order multi-agent systems with initial shifts, [39] applied adaptive iterative learning control methods to complete asymptotic tracking. G. Li et al.: ILC for Nonlinear MASs With Initial Shifts multi-agent systems in the directed network that didn’t meet the global Lipschitz condition, if the range of initial shifts variation gradually narrows, [41] proposed a robust cooperative learning strategy to carry out the high-precision formation. Aimed at high-order linear multi-agent with initial shifts, if the system information is accurately known, [42] utilized compression mapping method to execute the consensus tracking with the help of initial correction. A simulation example is given to illustrate the effectiveness of the presented algorithm
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