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Abstract
This paper is dedicated to addressing the predefined-time consensus tracking control problem for unknown high-order nonlinear multiagent systems. The prominent difference compared with some existing papers is that the follower is modeled in the form of non-strict-feedback structure. Meanwhile, instead of being constants, the real control gains are unknown functions. A distinct advantage in our work is that the outputs of followers are able to track the output of leader within the time specified in advance. In order to get our desired predefined-time controller, radial basis function (RBF) neural networks (NNS) are applied to compensate those unknown nonlinearities. Then in the design framework of adaptive backstepping, the predefined-time virtual control laws are presented and their derivatives are approximated by using finite-time differentiators. Under our proposed predefined-time controller, it is rigorously demonstrated that the whole closed-loop system remains stable and all outputs of the followers track the reference signal in predefined time. In the end, a simulation example is given to ulteriorly verify the efficacy of the suggested predefined-time control scheme.
Highlights
Consensus tracking control problem for distributed multiagent systems (MASs) has attracted a lot of attention from many great experts and scholars during the past years
We aim to develop a predefined-time adaptive consensus tracking control scheme for MASs to guarantee that all the signals in the MASs remain bound and the outputs of the followers well follow the output of the leader in our predefined time
This paper is committed to solving the predefined-time adaptive neural-networks-based consensus tracking control problem for high-order nonlinear MASs to achieve the tracking errors converge to zero within predefined time, in which each follower is modeled by the non-strict-feedback system
Summary
Consensus tracking control problem for distributed multiagent systems (MASs) has attracted a lot of attention from many great experts and scholars during the past years. Inspired by the above discussions, we propose a novel predefined-time adaptive neural-networks-based control scheme to solve the leader-following consensus tracking problem of the high-order nonlinear non-strict-feedback MASs. To sum up, the main contributions in our paper can be listed as follows: (i) Firstly, under our proposed controller, the outputs of the followers are able to track the output of leader within the time specified in advance, which addresses a momentous design obstacle in the field of control. (ii) Secondly, in spite of having the non-strict-feedback structure of each follower, the considered predefined-time adaptive control scheme still ensures that the tracking errors converge to zero instead of a small neighborhood of zero. This means our proposed control scheme has an even bigger superiority than the ones in [46], [47]. (iii) We apply finite-time differentiators to approximate the derivatives of the virtual control laws instead of directly using the differentiators of the virtual control laws in the design procedure of backstepping, which successfully avoid the problem of “ explosion of complexity”
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