Adaptive Neural Network Fixed-Time Leader–Follower Consensus for Multiagent Systems With Constraints and Disturbances

Ni, Junkang; Shi, Peng

doi:10.1109/tcyb.2020.2967995

Cited by 122 publications

(44 citation statements)

References 52 publications

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“…In comparison with the works in [6], [26], the fixed-time consensus control scheme based on the AFTDEO achieves that the tracking errors converge to a small region around the origin in fixed time rather than UUB. Compared to the results in [24], [27], the proposed consensus control scheme can operate well in absence of the secondorder state measurements of the leader and followers and in presence of measurement noises. Therefore, the proposed control scheme is really novel and practical.…”

Section: Introductionmentioning

confidence: 83%

“…Remark 2: Different from the existing fixed-time observers in [12], [13], [15], [24], the designed AFTDEO can not only achieve the convergences of the observation errors to the origin in fixed time, but also simultaneously suppress the leader and followers' measurement noises and reconstruct the disagreement error within fixed time only depending on the first-order state measurements of the leader and followers.…”

Section: Remarkmentioning

confidence: 99%

“…Remark 6: Based on the proposed AFTDEO which reconstructs the disagreement error for each follower in fixed time, the constructed consensus control scheme can achieve consensus for the second-order nonlinear multi-agent systems under directed topology graphs. As a matter of fact, most existing researches on fixed-time consensus/formation control for second-order multi-agent systems are conducted under undirected topology graphs [13], [24], [27], [35]. Since directed topologies are more practical than undirected topologies, the obtained results are superior to these results.…”

Section: B Fixed-time Consensus Control Law Design and Analysismentioning

confidence: 99%

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Observer-Based Fixed-Time Consensus Control for Nonlinear Multi-Agent Systems Subjected to Measurement Noises

Xiong

2020

IEEE Access

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This paper investigates the fixed-time consensus control problem for nonlinear secondorder multi-agent systems subjected to measurement noises and in absence of the second-order state measurements of the leader and the followers under directed communication topology graphs. For each follower, an innovative augmented fixed-time disagreement error observer (AFTDEO) is first designed to reconstruct the disagreement error within fixed time. On one hand, the proposed AFTDEO can not only operate well only depending on the noisy measurements of the first-order states of the leader and the followers, but also suppress the measurement noises of the leader and the followers. On the other hand, the proposed AFTDEO can guarantee that the observation errors are convergent to the origin in fixed time independent of the initial observation errors. Based on the proposed AFTDEO, a novel fixed-time consensus control scheme under a directed graph having a spanning tree is constructed to address the consensus problem without the second-order measurements of the leader and the followers by utilizing fixed-time nonsmooth backstepping technique. The fixed-time convergence of the tracking error is guaranteed through Lyapunov stability analysis. Finally, simulation results demonstrate the effectiveness of the proposed consensus control scheme.

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Section: Introductionmentioning

confidence: 83%

Section: Remarkmentioning

confidence: 99%

Section: B Fixed-time Consensus Control Law Design and Analysismentioning

confidence: 99%

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Observer-Based Fixed-Time Consensus Control for Nonlinear Multi-Agent Systems Subjected to Measurement Noises

Xiong

2020

IEEE Access

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“…Neural network (NN) is an efficient self-learning tool to counteract the adverse effect of unmodeled dynamics [29]- [32]. During the past decades, NN-based adaptive estimation algorithms have drawn much attentions, and a great deal of research advancements have been made in [33]- [38]. For instance, in [33], a radial basis function (RBF) neural network is employed for multiagent systems to neutralize unknown system dynamics and external disturbances.…”

Section: Introductionmentioning

confidence: 99%

“…During the past decades, NN-based adaptive estimation algorithms have drawn much attentions, and a great deal of research advancements have been made in [33]- [38]. For instance, in [33], a radial basis function (RBF) neural network is employed for multiagent systems to neutralize unknown system dynamics and external disturbances. A constrained backstepping adaptive NN control scheme is presented in [35] for MIMO aeroelastic systems to approximate the system uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Distributed adaptive formation control for underactuated quadrotors with guaranteed performances

Shao

Yue

2021

Nonlinear Dyn

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This paper investigates a distributed adaptive formation control problem for underactuated quadrotors with guaranteed performances. To ensure a robust and stable formation pattern with predefined behavior bounds, by transforming the original constrained formation synchronization error dynamics into an equivalent unconstrained one, a prescribed performance mechanism is introduced in the translational loop to render the formation regulation as aprior. An adaptive consensus strategy is developed according to undirected graph theory and Lyapunov stability rules for follower quadrotors to achieve a distributed cooperative formation with prescribed tracking abilities via exchanging local information with neighbors. The presented control scheme has the following salient merits: 1) the formation synchronization errors can be guaranteed within pre-assigned bounds with desired transient behaviors despite of uncertain disturbances; 2) by using a state estimation error to update neural network (NN) parameters, rather than the tracking error that widely applied in traditional NN approximators, and with the help of MLP technique, the proposed SE-MLP observer capable of decreasing the computational complexity can achieve a fast identification of lumped disturbances without causing high-frequency oscillations even using a large adaptive gain, and the transient solutions of L 2 norm of the differential of neural weights are established to illustrate the mechanism of SE-MLP observer in reducing chattering behaviors. The merits of presented algorithm are confirmed by sufficient simulations.

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Adaptive neural network observer‐based filtered backstepping control for nonlinear systems with fuzzy dead‐zone and uncertainty

Shahriari-kahkeshi

Jahangiri‐heidari

Shi

2023

Adaptive Control & Signal

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This study proposes a high gain observer-based filtered backstepping control scheme for nonlinear systems with unknown dynamics and dead-zone constraint. Majority of the previously papers describe dead-zone by the conventional certain models, assume that all state variables are available and invoke the backstepping or conventional dynamic surface control (CDSC) approaches for controller design. This work describes the dead-zone by the fuzzy model, invokes radial basis function neural network (RBFNN) to model the unknown functions and proposes a RBFNN-based high gain observer to estimate unmeasurable states. To avoid the complexity explosion in the backstepping approach and eliminate the sensitivity to the time-constant of the first-order filters in the CDSC, a nonlinear tracking differentiator (NTD) with finite time convergence property is used instead of the first-order filters in the CDSC to obtain derivative of the virtual inputs. Moreover, the error compensation mechanism and auxiliary signal are proposed to eliminate the influence of the filtering error and input nonlinearity, respectively. Despite the fuzzy dead-zone, the proposed scheme makes the closed-loop signals uniformly ultimately bounded. In comparison with the existing results, (i) some assumptions like certain model of dead-zone and full state measurement are removed, (ii) "explosion of complexity" and sensitivity to the time constant of the first-order filters are eliminated, (iii) effect of the filtering error and input nonlinearity are compensated, (iv) number of adjustable parameters and online computational burden are decreased effectively. Simulation results on the two well-known examples verify the effectiveness and applicability of the proposed new design technique.

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Adaptive Neural Network Fixed-Time Leader–Follower Consensus for Multiagent Systems With Constraints and Disturbances

Cited by 122 publications

References 52 publications

Observer-Based Fixed-Time Consensus Control for Nonlinear Multi-Agent Systems Subjected to Measurement Noises

Observer-Based Fixed-Time Consensus Control for Nonlinear Multi-Agent Systems Subjected to Measurement Noises

Distributed adaptive formation control for underactuated quadrotors with guaranteed performances

Adaptive neural network observer‐based filtered backstepping control for nonlinear systems with fuzzy dead‐zone and uncertainty

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