Optimal Output Consensus for Nonlinear Multiagent Systems With Both Static and Dynamic Uncertainties

Tang, Yutao; Wang, Xinghu

doi:10.1109/tac.2020.2996978

Cited by 60 publications

(73 citation statements)

References 30 publications

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“…Thus, the optimal signal generator proposed in Tang et al 21 fails to achieve our goal without the information of

L^{⊤}

. To tackle this issue, the following optimal signal generator has been developed 24 for problem (2) under weighted‐balanced graphs:

\begin{align} {\overset{z}{true}}_{i} & = prefix- α normal∇ f_{i} false(z_{i} false) prefix- β \sum_{j = 1}^{N} a_{i j} false(z_{i} prefix- z_{j} false) + \sum_{j = 1}^{N} a_{i j} false(v_{i} prefix- v_{j} false) \\ {\overset{v}{true}}_{i} & = α β \sum_{j = 1}^{N} a_{i j} false(z_{i} prefix- z_{j} false) \end{align}

with some chosen parameters

α, β > 0

. Its effectiveness has already been established in the work of Tang et al 24 .…”

Section: Resultsmentioning

confidence: 99%

“…To tackle this issue, the following optimal signal generator has been developed 24 for problem (2) under weighted‐balanced graphs:

\begin{align} {\overset{z}{true}}_{i} & = prefix- α normal∇ f_{i} false(z_{i} false) prefix- β \sum_{j = 1}^{N} a_{i j} false(z_{i} prefix- z_{j} false) + \sum_{j = 1}^{N} a_{i j} false(v_{i} prefix- v_{j} false) \\ {\overset{v}{true}}_{i} & = α β \sum_{j = 1}^{N} a_{i j} false(z_{i} prefix- z_{j} false) \end{align}

with some chosen parameters

α, β > 0

. Its effectiveness has already been established in the work of Tang et al 24 . Here we denote

\underline{l} = \min_{i} {l_{i}}

\overline{l} = \max_{i} {{\overline{l}}_{i}}

and present a sketch of proof for a complete design.…”

Section: Resultsmentioning

confidence: 99%

“…To handle the parametric uncertainties, there are at least two standard strategies in the literature, that is, adaptive control policy with a dynamic compensator and robust control policy assuming these parameters are bounded. Both strategies have been utilized to tackle the considered optimal consensus problem for several classes of multi‐agent systems having uncertain parameters 22‐26 . However, most existing results require the full state of each agent except the work for relative‐degree‐one agents 22 .…”

Section: Introductionmentioning

confidence: 99%

“…However, most existing results require the full state of each agent except the work for relative‐degree‐one agents 22 . Although this work has been extended to the case when agents are in normal form, it still requires the partial states for feedback 24 . Note that the state information may not be available in practice due to physical constraints and high measurement costs 27‐30 .…”

Section: Introductionmentioning

confidence: 99%

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Optimal consensus for uncertain high‐order multi‐agent systems by output feedback

Tang

Zhu

2021

Intl J Robust & Nonlinear

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The distributed optimal output consensus problem for high‐order multi‐agent systems has been studied recently. In this article, we further focus on the same problem for high‐order multi‐agent systems subject to parametric uncertainties and aim at distributed robust controllers by measurement output feedback. We first develop a dynamic compensator to estimate the expected optimal consensus point and convert the problem into several decentralized robust tracking problems. Then, by combining the integral control technique and dirty derivative observer technique, we constructively propose a distributed output feedback integral controller to solve this problem under a mild graph connectivity condition.

show abstract

“…Thus, the optimal signal generator proposed in Tang et al 21 fails to achieve our goal without the information of

L^{⊤}

. To tackle this issue, the following optimal signal generator has been developed 24 for problem (2) under weighted‐balanced graphs:

\begin{align} {\overset{z}{true}}_{i} & = prefix- α normal∇ f_{i} false(z_{i} false) prefix- β \sum_{j = 1}^{N} a_{i j} false(z_{i} prefix- z_{j} false) + \sum_{j = 1}^{N} a_{i j} false(v_{i} prefix- v_{j} false) \\ {\overset{v}{true}}_{i} & = α β \sum_{j = 1}^{N} a_{i j} false(z_{i} prefix- z_{j} false) \end{align}

with some chosen parameters

α, β > 0

. Its effectiveness has already been established in the work of Tang et al 24 .…”

Section: Resultsmentioning

confidence: 99%

“…To tackle this issue, the following optimal signal generator has been developed 24 for problem (2) under weighted‐balanced graphs:

\begin{align} {\overset{z}{true}}_{i} & = prefix- α normal∇ f_{i} false(z_{i} false) prefix- β \sum_{j = 1}^{N} a_{i j} false(z_{i} prefix- z_{j} false) + \sum_{j = 1}^{N} a_{i j} false(v_{i} prefix- v_{j} false) \\ {\overset{v}{true}}_{i} & = α β \sum_{j = 1}^{N} a_{i j} false(z_{i} prefix- z_{j} false) \end{align}

with some chosen parameters

α, β > 0

. Its effectiveness has already been established in the work of Tang et al 24 . Here we denote

\underline{l} = \min_{i} {l_{i}}

\overline{l} = \max_{i} {{\overline{l}}_{i}}

and present a sketch of proof for a complete design.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal consensus for uncertain high‐order multi‐agent systems by output feedback

Tang

Zhu

2021

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

show abstract

“…Two consensus problems were discussed in References 8 and 9 for the high‐order nonlinear multiagent systems with unknown dead‐zone input and directed switching topology, respectively, and the disturbed protocols was established in Reference 10 to investigate the leader‐following consensus problem for the nonlinear multiagent system with unmodeled dynamics. The consensus problems was solved in Reference 11 for the heterogeneous uncertain nonlinear multiagent systems with high‐order nonlinear dynamics. The cooperative semi‐global output regulation results were presented in Reference 12 for the nonlinear strict‐feedback multiagent system with switching network topologies.…”

Section: Introductionmentioning

confidence: 99%

Practical finite‐time sampled‐data output consensus for a class of nonlinear multiagent systems via output feedback

Mao

Huang

Xiang

et al. 2020

Intl J Robust & Nonlinear

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This article devotes a practical finite‐time sampled‐data output consensus investigation for a class of nonlinear multiagent systems with unavailable velocities and undirected topology via output feedback. Noticing that the focused multiagent system features nonlinearities with non‐Lipschitz restriction, and the outputs of the agents are detectable only at the sampling instants, a reduced‐order observer with the sampled output measurement is established for each follower to evaluate the unavailable velocity, and the distributed finite‐time sampled‐data consensus protocols, which are constructed by the established reduced‐order observer as well as the sampled output measurement of the agents, are developed by combining the techniques of adding a power integral and backstepping. By selecting a suitable Lyapunov function candidate, we can verify that the developed sampled‐data consensus protocols which work under the allowable sampling period and the appropriate design parameters can solve the considered practical finite‐time output consensus problem. Finally, two simulation examples, include a numerical and an engineering simulations, are provided to reveal the effectiveness and availability of the proposed consensus scheme.

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Fixed‐time distributed adaptive optimization for third‐order nonlinear fully heterogeneous vehicular platoon systems

Lei,

2024

Adaptive Control & Signal

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SummaryIn this article, the problem of fixed‐time distributed optimization is researched for third‐order fully heterogeneous nonlinear connected and autonomous vehicles. To address this problem, a fixed‐time distributed optimization algorithm is proposed via a two‐step strategy. First, an optimization algorithm is proposed to generate a virtual reference signal that can converge to the optimal solution. Then, we further construct an adaptive tracking controller to track the virtual signal based on the first step. In the design process of the controller, the virtual control variables and the actual control input are obtained by using the backstepping technique. Furthermore, a fast fixed‐time filter is introduced to avoid the issue of discontinuous gradient functions. Fuzzy logic systems are employed to address the unknown part of the nonlinear function. By using tools from the fixed‐time stability criterion, convex optimization theory, and Lyapunov stability theory, it can be demonstrated that the provided strategy drives all optimization variables to the optimal solution within a fixed time and guarantees the closed‐loop system signals are bounded. Finally, the effectiveness of control strategy is verified via a numerical simulation.

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Optimal Output Consensus for Nonlinear Multiagent Systems With Both Static and Dynamic Uncertainties

Cited by 60 publications

References 30 publications

Optimal consensus for uncertain high‐order multi‐agent systems by output feedback

Optimal consensus for uncertain high‐order multi‐agent systems by output feedback

Practical finite‐time sampled‐data output consensus for a class of nonlinear multiagent systems via output feedback

Fixed‐time distributed adaptive optimization for third‐order nonlinear fully heterogeneous vehicular platoon systems

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