Distributed adaptive fuzzy iterative learning control of coordination problems for higher order multi-agent systems

Abstract

In this paper, the adaptive fuzzy iterative learning control scheme is proposed for coordination problems of Mth order (M ≥ 2) distributed multi-agent systems. Every follower agent has a higher order integrator with unknown nonlinear dynamics and input disturbance. The dynamics of the leader are a higher order nonlinear systems and only available to a portion of the follower agents. With distributed initial state learning, the unified distributed protocols combined time-domain and iteration-domain adaptive laws guarantee that the follower agents track the leader uniformly on [0, T]. Then, the proposed algorithm extends to achieve the formation control. A numerical example and a multiple robotic system are provided to demonstrate the performance of the proposed approach.

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

“…The main differences between these papers lie in the conditions of the communication graphs (ie, on the information‐exchange topology). In addition, the Lyapunov function and composite energy function (CEF) are both useful tools for handing parameterized nonlinear systems such as those investigated for the applications given in other works . These techniques have shown particular advantages in the design and analysis of ILC for MAS.…”

“…For this type of nonlinear agent model, a feasible way is to approximate the nonlinearity by a universal approximator such as fuzzy systems and neural networks. Few papers have reported this treatment except the works of Li and Li . In comparison with the aforementioned works, we have the following distinctions.…”

“…Few papers have reported this treatment except the works of Li and Li . In comparison with the aforementioned works, we have the following distinctions. First, in the works of Li and Li, the control gain is fixed to be one in order to facilitate the subsequent analysis, whereas in this paper, the control gain is extended to general, unknown, time‐varying, and state‐dependent gain function.…”

“…In addition, the Lyapunov function and composite energy function (CEF) are both useful tools for handing parameterized nonlinear systems such as those investigated for the applications given in other works. [33][34][35][36][37][38][39][40] These techniques have shown particular advantages in the design and analysis of ILC for MAS.…”

“…In this paper, we provide an alternative hyperbolic tangent function and establish a finite-iteration convergence result of the tracking error to a predefined neighborhood of zero. In summary, although we employ the CEF technique for convergence analysis similar to the other works, [33][34][35][36][37][38][39][40] the contributions in this paper cannot be covered by the existing studies.…”