Distributed Adaptive Neural Consensus Control for Stochastic Nonlinear Multiagent Systems with Whole State Delays and Mu
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ISSN:1598-6446 eISSN:2005-4092 http://www.springer.com/12555
Distributed Adaptive Neural Consensus Control for Stochastic Nonlinear Multiagent Systems with Whole State Delays and Multiple Constraints Yukun Tao, Feifei Yang*, Ping He, Congshan Li, and Yuqi Ji Abstract: This paper presents a distributed adaptive neural tracking consensus control strategy for a class of stochastic nonlinear multiagent systems with whole state time delays, input and output constrains. The considered systems are involved in the existence of whole state delays and stochastic disturbances, which makes the controller design more difficult and complex. Firstly, time delays are related to unknown dynamic interactions with the whole states of the agent systems, and novel Lyapunov-Krasovskii functionals are constructed. Secondly, the smooth asymmetric saturation nonlinearity is given based on Gaussian error function, output constraints are achieved via barrier Lyapunov functions, and neural networks are utilized to deal with the completely unknown nonlinearities and stochastic disturbances. Then, based on Lyapunov stability theory, a delay-independent adaptive controller is developed via Lyapunov-Krasovskii functionals and backstepping technique, and it reduces the complexity of learning parameters. It is proved that the proposed approximation-based controller can guarantee that all closed-loop signals are cooperatively semi-globally uniformly ultimately bounded (CSGUUB), and the tracking errors between the followers and the leaders eventually converge to a small neighbourhood around the origin. Finally, simulation studies are carried out, and the simulation results verify the correctness and effectiveness of the proposed strategy. Keywords: Adaptive neural control, distributed output tracking control, Lyapunov-Krasovskii functionals, stochastic disturbance, whole state time delay.
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INTRODUCTION
Multiagent systems (MASs) are usually employed to achieve coordinated tasks by letting a group of agents operate cooperatively with each other. As an important problem in distributed coordination of MASs, consensus has caused compelling concern during the last decade due to its wide applications in various fields, such as unmanned air vehicles, sensor networks, power systems, etc [1, 2]. In practical systems, time delays and stochastic disturbances are often found, which may deteriorate the control system performance, and even disrupt the stability [3, 4]. Furthermore, most practical systems can achieve higher operational and economic benefits near the boundary, and thus it is worth studying the constrained control. Therefore, this paper concerns the distributed approximation-based consensus for uncertain MASs with time delays and stochastic disturbances. In the research on consensus tracking problem, the early works focused on linear MASs. Nevertheless, unknown
nonlinear behaviour exists in many practical physics systems, which makes the consensus tracking problem more challenging and trips the classical nonlinear control strategies. Fortunatel
