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- W4313252308 abstract "In this study, an adaptive data-driven control protocol design scheme based on higher order parameter (HOP) estimation and iterative learning is proposed for a class of nonaffine multi-agent systems (MAS) with unknown nonlinearity to solve the consistency tracking problem of MAS with fixed and switching topology communications. The control protocol mainly comprises HOP estimation and an iterative learning controller. The iterative learning control (ILC) algorithm is mainly used for system synergy, and the adaptive control algorithm based on HOP estimation mainly completes the system stabilization. The main advantage of the control protocol is that it uses only the input/output (I/O) data of the agents to complete the consensus tracking task and does not require specifying the dynamical system of each agent. The control protocol has a modular design, in which the iterative learning and HOP estimation algorithms complement each other without affecting the structure of the controller. The effectiveness of the control protocol is demonstrated using two simulation experiments." @default.
- W4313252308 created "2023-01-06" @default.
- W4313252308 creator A5061167023 @default.
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- W4313252308 date "2023-02-01" @default.
- W4313252308 modified "2023-10-16" @default.
- W4313252308 title "Design of data-driven mode-free iterative learning controller based higher order parameter estimation for multi-agent systems consistency tracking" @default.
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- W4313252308 doi "https://doi.org/10.1016/j.knosys.2022.110221" @default.
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