Abstract
This paper addresses a consensus problem for uncertain nonlinear multiagent systems with predefined precision under disturbance. By employing the neural networks method and backstepping technique, adaptive controllers for each agent are created. In contrast to the exiting global control methods for multiagent systems, global precision consensus control scheme is first put forward. Moreover, by using three n th-order continuous differentiable functions, adaptive tuning laws and virtual controllers and the real controller are designed. It is proved that the presented method can ensure that all signals are globally bounded and systems can be consistent with a given accuracy under disturbance. Finally, a practical simulation verifies the correctness for the devised control protocol.
Highlights
Over the last few decades, multiagent systems (MASs) have become a hot research direction because of widespread application in various areas such as mobile robots, cooperative surveillance, spacecraft formation flying, and sensor networks [1,2,3]. e consensus is a key issue in the study of MASs; it requires that all agents’ states tend to a same value
With the development of research, the study for leaderfollower has appeared in many aspects of MASs
For uncertain nonlinear MASs, estimation-based strategies are often used to cope with unknown items in dynamic models [30,31,32,33,34,35,36,37,38,39,40]. ese mean that both fuzzy logic systems (FLSs) and neural networks (NNs) can be utilized to estimate uncertain nonlinearities in MASs through the backstepping technique
Summary
Over the last few decades, multiagent systems (MASs) have become a hot research direction because of widespread application in various areas such as mobile robots, cooperative surveillance, spacecraft formation flying, and sensor networks [1,2,3]. e consensus is a key issue in the study of MASs; it requires that all agents’ states tend to a same value. For uncertain nonlinear MASs, estimation-based strategies are often used to cope with unknown items in dynamic models [30,31,32,33,34,35,36,37,38,39,40]. Ese mean that both fuzzy logic systems (FLSs) and neural networks (NNs) can be utilized to estimate uncertain nonlinearities in MASs through the backstepping technique. N; in [34, 35], FLSs were used to approximate nonlinear uncertain items in dynamic models; the work in [36] showed that the approximator WTφ(x) is devised to replace the unknown function y(x). Adaptive control technique and backstepping technique were common approaches to design the controller and adaptive laws
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