Adaptive consensus control for stochastic nonlinear multiagent systems with full state constraints

Xiao, Wenbin; Cao, Liang; Dong, Guowei; Bai, Weiwei; Zhou, Qi

doi:10.1002/rnc.4831

Cited by 23 publications

(19 citation statements)

References 53 publications

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“…Remark that when the MASs suffer multiple disturbances with different periods, whether the developed strategy still works, or how to refine the strategy for finite‐time compensation and consensus, both are what we pursue in the future study. Moreover, developing the proposed strategy into the more general form of systems or more general disturbances (such as unbounded ones) is also an important topic we will discuss in the future 39‐43 …”

Section: Discussionmentioning

confidence: 99%

Asymptotic consensus via switching adaptive for nonlinear multi‐agent systems subject to atypical disturbances

Liu

2021

Adaptive Control & Signal

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Summary The paper is devoted to the asymptotic output consensus for a class of uncertain nonlinear multi‐agent systems. The disturbance that the system suffered is said to be “atypical" since, unlike the related literature, it is unnecessarily continuously differentiable (e.g., sawtooth wave), and even permitted to be discontinuous (e.g., square wave), despite requiring it to be of unknown integer multiples of certain period. Typically, the multi‐agent systems admit more than one ingredient causing heterogeneities: (i) No identical requirement is made on the system nonlinearities and input directions (the unknown signs of the input coefficients). (ii) The orders of agent dynamics can be different (the dynamics are first‐order, second‐order, or even high‐order). Nevertheless, in the context, we still pursue the asymptotic output consensus, and unlike some related literature, instead of seeking a time‐varying strategy which means the introduction of infinitely large gains, we combine a refined switching strategy with continuous adaptive one together to propose a new consensus protocol. It turns out that under the proposed protocol, all the agent outputs reach a common value while all the closed‐loop signals are bounded. Simulation examples are given to demonstrate the effectiveness of the proposed protocol.

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Section: Discussionmentioning

confidence: 99%

Asymptotic consensus via switching adaptive for nonlinear multi‐agent systems subject to atypical disturbances

Liu

2021

Adaptive Control & Signal

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“…Remark The Assumption 1 is also given in References 24‐27, and from Assumption 1, we know that both functions

h_{i, p}^{(q)} (\cdot)

and

f_{i, p}^{(q)} (\cdot)

are each Lipschitz on

Ω_{d}

. The Assumption 2 is a general assumption for solving consensus tracking problems of MASs as in References 22,23,26,27, since it can guarantee the conclusion in Lemma 1 is hold.…”

Section: System Descriptions and Preliminariesmentioning

confidence: 99%

“…As a response method, the dynamic surface control solves the problem of explosion of complexity by using command filter effectively, 19‐21 and the fuzzy logic systems (FLSs) are introduced to estimate the unknown nonlinear dynamics. The dynamic surface control was extended to solve the consensus problems for full‐sate constrained MASs in References 22,23, but how to deal with the filtering errors caused by the filtering process is not considered, which will reduce the control performance. To solve this problem, the command filtered backstepping with error compensation mechanism has been studied for nonlinear systems in References 24,25 and for MASs in References 26,27, which ensures the filtering performance with the elimination of filtering errors.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Compared with the backstepping based asymptotically convergent tracking control methods for state constrained nonlinear systems in References 28,29 and asymptotically convergent consensus tracking control methods for state constrained nonlinear MASs in References 22,23,30, the finite‐time consensus tracking control problem is further studied, which can guarantee the closed‐loop system has faster convergence rate. What's more, the nonstrict feedback form is further considered, which is more appropriate to the actual systems. Compared with the command filtered backstepping methods in References 24,26 and finite‐time command filtered backstepping methods in References 25,27,33,35, the full‐state constraints are considered, which cannot only ensure the desired consensus tracking performance, but also guarantee the predefined state constraints are not violated.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive finite‐time consensus tracking control for nonlinear multiagent systems in nonstrict feedback form with full‐state constraints

Liu

Zhao

2021

Adaptive Control & Signal

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Summary In this article, the fuzzy adaptive finite‐time consensus tracking control problem for nonstrict feedback nonlinear multiagent systems with full‐state constraints is studied. The finite‐time control based on command filtered backstepping is proposed to guarantee the finite‐time convergence and eliminate the explosion of complexity problem caused by backstepping process, and the errors in the filtering process are compensated by using error compensation mechanism. Furthermore, based on the fuzzy logic systems, the uncertain nonlinear dynamics are approximated and the problem of state variables in nonstrict feedback form is solved by using the property of basis functions. The barrier Lyapunov functions are introduced to guarantee that all system states and compensated tracking error signals are constrained in the designed regions. A simulation example is given to verify the superiority of the proposed algorithm.

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Optimized adaptive event‐triggered tracking control for multi‐agent systems with full‐state constraints

Yang

Pan

Sun

et al. 2022

Intl J Robust & Nonlinear

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In this article, the event‐triggered optimized adaptive tracking control problem is investigated for a class of multi‐agent systems subject to full‐state constraints. To address the full‐state constraints problem, a nonlinear mapping technique is applied, which can release the feasibility conditions for virtual controllers. Based on the mean‐value theorem, the nonaffine nonlinear terms generated by transformed system are separated, which overcomes the obstacle of solving optimized solution. The neural network based reinforcement learning (RL) algorithm with the identifier‐critic‐actor architecture is introduced to obtain the optimal solution of systems with unknown dynamics. It is worth noting that the RL algorithm in this article is simplified, which can reduce the computational burden. To reducing the communication burden, an event‐triggered mechanism with time‐varying threshold related to the optimized control signal is developed. By applying the Lyapunov stability method, it is proved that the desired optimized tracking performance and the stability of the closed‐loop systems can be guaranteed. Finally, a simulation example demonstrates that the proposed control strategy is effective.

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Adaptive consensus control for stochastic nonlinear multiagent systems with full state constraints

Cited by 23 publications

References 53 publications

Asymptotic consensus via switching adaptive for nonlinear multi‐agent systems subject to atypical disturbances

Asymptotic consensus via switching adaptive for nonlinear multi‐agent systems subject to atypical disturbances

Adaptive finite‐time consensus tracking control for nonlinear multiagent systems in nonstrict feedback form with full‐state constraints

Optimized adaptive event‐triggered tracking control for multi‐agent systems with full‐state constraints

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