Model-Free Formation Control: Multi-Input Iterative Learning Super-Twisting Approach
提出一种经济型无模型超螺旋控制算法，结合迭代学习控制，用于奇异摄动多智能体系统的编队控制，通过输入输出数据学习未知重复不确定性，实现满意的共识跟踪性能。
This work proposes an economic model-free super twisting control (STWC) algorithm for the FCT of a singularly perturbed MAS. Specifically, the intelligent model-free control framework is designed to be the sum of a MISTWC and an iterative learning control (ILC). First, time scales are artificially introduced into the STWC for the multiagent formation construction, without overestimating the control gains. Then, the input-output data collected from the iterative experiments are used to learn the model of unknown repeated uncertainties, and drive the whole system toward satisfactory consensus tracking performance. By utilizing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon$</tex-math> </inline-formula> -dependent Lyapunov method, the convergence properties of the STWC-type ILC are rigorously analyzed in both the iteration domain and the time domain. The selection method of the design parameters is also provided. Simulation results validate the effectiveness of the proposed controller in terms of formation construction, trajectory tracking, and robustness to system uncertainties.