Multi‐Tracking of First Order Multi‐Agent Networks Via Self‐Triggered Control

Han, Guang-Song; Guan, Zhi‐Hong; Chen, Jie; He, Dingxin; Chi, Ming

doi:10.1002/asjc.1003

Cited by 9 publications

(5 citation statements)

References 24 publications

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“…When agent

i

competes with agent

j

ξ_{i j} = - 1

; when agent

i

abstains from agent

j

ξ_{i j} = 0

; when agent

i

cooperates with agent

j

ξ_{i j} = 1

. The intelligent factor is determined according to the following two principles with a priority [10–12] This case

ξ_{i j} = 0

should be as few as possible. For all

i \in V, \sum_{j = r_{k}}^{r_{k}^{'}} {\bar{l}}_{i j} = 0

, where

r_{k} = 1 + \sum_{i = 1}^{k} m_{i - 1}

and

r_{k}^{'} = \sum_{i = 1}^{k} m_{i}

. Furthermore

\overset{false¯}{L} = (][, \begin{matrix} {\bar{scriptL}}_{11} & {\bar{scriptL}}_{12} & \dots & {\bar{scriptL}}_{1 r} \\ {\bar{scriptL}}_{21} & {\bar{scriptL}}_{22} & \dots & {\bar{scriptL}}_{2 r} \\ ⋮ & ⋱ & ⋱ & ⋮ \\ {\bar{scriptL}}_{r 1} & scriptL \end{matrix})

…”

Section: Problem Formulationmentioning

confidence: 99%

“…The multiple warheads can be grouped to attack several goals simultaneously, which improve the operational effectiveness. In [10–12], Han et al have begun to study this problem. Unfortunately, almost all of the aforementioned works are based on the conservative case that agents are modelled by single‐integrator or double‐integrator homogeneous dynamics.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi‐tracking control of heterogeneous multi‐agent systems with single‐input– single‐output based on complex frequency domain analysis

Zhang

Liu

Wang

2016

IET Control Theory & Applications

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“…When agent

i

competes with agent

j

ξ_{i j} = - 1

; when agent

i

abstains from agent

j

ξ_{i j} = 0

; when agent

i

cooperates with agent

j

ξ_{i j} = 1

. The intelligent factor is determined according to the following two principles with a priority [10–12] This case

ξ_{i j} = 0

should be as few as possible. For all

i \in V, \sum_{j = r_{k}}^{r_{k}^{'}} {\bar{l}}_{i j} = 0

, where

r_{k} = 1 + \sum_{i = 1}^{k} m_{i - 1}

and

r_{k}^{'} = \sum_{i = 1}^{k} m_{i}

. Furthermore

\overset{false¯}{L} = (][, \begin{matrix} {\bar{scriptL}}_{11} & {\bar{scriptL}}_{12} & \dots & {\bar{scriptL}}_{1 r} \\ {\bar{scriptL}}_{21} & {\bar{scriptL}}_{22} & \dots & {\bar{scriptL}}_{2 r} \\ ⋮ & ⋱ & ⋱ & ⋮ \\ {\bar{scriptL}}_{r 1} & scriptL \end{matrix})

…”

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi‐tracking control of heterogeneous multi‐agent systems with single‐input– single‐output based on complex frequency domain analysis

Zhang

Liu

Wang

2016

IET Control Theory & Applications

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“…However, in many practical circumstances, the main results of the above literature studies cannot be directly applied if the networked systems are required to execute multiple tasks concurrently. A few works centralized on multiple tracking [7,36,37], multiple formation [16,17], and group time-varying formation [19] are thus activated. To the best of our knowledge, the time-varying formation tracking problem of NHRSs in the task space remains unsolved, let alone the multiple time-varying formation tracking problem.…”

Section: Introductionmentioning

confidence: 99%

Multiple Time-Varying Formation of Networked Heterogeneous Robotic Systems via Estimator-Based Hierarchical Cooperative Algorithms

Liang

Zhan

et al. 2020

Complexity

Self Cite

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This paper investigates both the time-varying formation and multiple time-varying formation tracking problems of networked heterogeneous robotic systems (NHRSs) with parameter uncertainties and external disturbances in the task space. Each robot inside can be either redundant or nonredundant. Several novel estimator-based hierarchical cooperative (EBHC) algorithms are designed to achieve both the tracking task and the possible preset subtasks for redundant robots. Besides, the designed estimator algorithms guarantee that each robot can obtain the accurate information of their corresponding leaders. By employing Lyapunov stability and input-to-state stability, sufficient conditions on the asymptotic stability of the error closed-loop system are derived. Finally, two simulation examples are presented to verify the effectiveness of the proposed algorithms.

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“…Recently, the consensus problem of multi-agent systems has been actively concerned. Many control protocols have been designed to solve the consensus problem of multi-agent systems with different conditions (Dibaji and Ishil, 2015; Han et al, 2015; Hong et al, 2013; Liu et al, 2014; Miao and Ma, 2015; Wang et al, 2017; Wang et al, 2016; Zhang et al, 2015). It should be pointed out that the model of multi-agent systems mentioned in the above literature is homogeneous, that is, all the agents have the same dynamics.…”

Section: Introductionmentioning

confidence: 99%

Distributed adaptive iterative learning control for the consensus tracking of heterogeneous nonlinear multi-agent systems

Deng

Sun

2020

Transactions of the Institute of Measurement and Control

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This paper addresses the consensus tracking problem of leader-following heterogeneous multi-agent systems with iterative learning control. The model of heterogeneous multi-agent systems consists of first-order and second-order nonlinear dynamics. It is assumed that only a portion of following agents can receive the leader’s information. The radial basis function neural network is introduced to deal with the nonlinear terms of following agents. Then, the distributed adaptive iterative learning control protocols with neural network are designed for following agents with different dynamics. Meanwhile, the adaptive update control laws for the time-varying parameters are proposed. Theoretical analysis shows that the consensus tracking problem of the given multi-agent systems can be guaranteed on the time domain and iterative domain. Finally, the validity of theoretical results is verified by a simulation example.

show abstract

Multi‐Tracking of First Order Multi‐Agent Networks Via Self‐Triggered Control

Cited by 9 publications

References 24 publications

Multi‐tracking control of heterogeneous multi‐agent systems with single‐input– single‐output based on complex frequency domain analysis

Multi‐tracking control of heterogeneous multi‐agent systems with single‐input– single‐output based on complex frequency domain analysis

Multiple Time-Varying Formation of Networked Heterogeneous Robotic Systems via Estimator-Based Hierarchical Cooperative Algorithms

Distributed adaptive iterative learning control for the consensus tracking of heterogeneous nonlinear multi-agent systems

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