Time‐varying formation of second‐order discrete‐time multi‐agent systems under non‐uniform communication delays and switching topology with application to UAV formation flying

Wang, Jianhua; Han, Liang; Li, Xiaoduo; Dong, Xiwang; Li, Qingdong; Zhang, Ren

doi:10.1049/iet-cta.2020.0183

Cited by 33 publications

(12 citation statements)

References 59 publications

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“…In practical application, the formation should be designed reasonably according to the needs of the actual task, the number of formation members, the hardware communication, the processing capability of the machine processor, the transition of the UAV group, and other aspects of logistics support, to achieve optimal mission efficiency [34].…”

Section: Structures Of Uav Formationmentioning

confidence: 99%

“…It can be seen from equation (34) that τ and ω are roughly inversely proportional to each other, and ω and λ k are directly proportional to each other, so the largest characteristic root λ max corresponds to the smallest delay; that is, the upper bound of the delay τ f is determined. When τ is less than τ f , the system is uniformly stable, and when τ is greater than or equal to τ f , the system is unstable.…”

Section: Formation Reference Position Command Formation Maneuver Commandmentioning

confidence: 99%

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A Leader‐Follower Formation Control of Multi‐UAVs via an Adaptive Hybrid Controller

et al. 2021

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The purpose of this study is to offer an adaptive hybrid controller for the formation control of multiple unmanned aerial vehicles (UAVs) leader-follower configurations with communication delay. Although numerous studies about the control of the formation exist, very few incorporate the delay in their model and are adaptive as well. The motivation behind this article is to bridge that gap. The strategy consists of an adaptive fuzzy logic controller and a Proportional, Integral, and Derivative (PID) controller where the logic controller fines/tunes the PID controller gains. The controller also consists of an integrator that raises the order of the system which helps reduce the noise and steady-state errors. The simulations confirm that the proposed technique is robust and satisfies mission requirements. Moreover, the flying formations of the swarm were created by a B-spline curve based on a simple waypoint. The main contribution of this study is to present a model where the formation remains stable during the whole flight, errors are within the optimal range, and the time delays are manageable.

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Section: Structures Of Uav Formationmentioning

confidence: 99%

Section: Formation Reference Position Command Formation Maneuver Commandmentioning

confidence: 99%

A Leader‐Follower Formation Control of Multi‐UAVs via an Adaptive Hybrid Controller

et al. 2021

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“…By employing the Lyapunov function method and LMIs technique, the sufficient existing conditions and solutions of control protocol are given in the form of LMIs. In [ 25 ], the problem of time-varying formation of second-order discrete-time MASs under switching topologies and the time delay is investigated. The sufficient conditions are given to ensure MASs accomplish the mission of time-varying formation based on the state transformation method.…”

Section: Introductionmentioning

confidence: 99%

Intelligent L2-L∞ Consensus of Multiagent Systems under Switching Topologies via Fuzzy Deep Q Learning

Cheng

Song

et al. 2022

Computational Intelligence and Neuroscience

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The problem of intelligent L2-L∞ consensus design for leader-followers multiagent systems (MASs) under switching topologies is investigated based on switched control theory and fuzzy deep Q learning. It is supposed that the communication topologies are time-varying, and the model of MASs under switching topologies is constructed based on switched systems. By employing linear transformation, the problem of consensus of MASs is converted into the issue of L2-L∞ control. The consensus protocol is composed of the dynamics-based protocol and learning-based protocol, where the robust control theory and deep Q learning are applied for the two parts to guarantee the prescribed performance and improve the transient performance. The multiple Lyapunov function (MLF) method and mode-dependent average dwell time (MDADT) method are combined to give the scheduling interval, which ensures stability and prescribed attenuation performance. The sufficient existing conditions of consensus protocol are given, and the solutions of the dynamics-based protocol are derived based on linear matrix inequalities (LMIs). Then, the online design of the learning-based protocol is formulated as a Markov decision process, where the fuzzy deep Q learning is utilized to compensate for the uncertainties and achieve optimal performance. The variation of the learning-based protocol is modeled as the external compensation on the dynamics-based protocol. Therefore, the convergence of the proposed protocol can be guaranteed by employing the nonfragile control theory. In the end, a numerical example is given to validate the effectiveness and superiority of the proposed method.

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“…The problems of consensus and consensus tracking are core problems of coordination control of MASs, whose objective lies in establishing a relationship between the agents, so as to accomplish common goals jointly by designing appropriate control protocol to achieve an agreement for the states/outputs [4][5][6]. Recently, fruitful and encouraging results are reported on the consensus problem of MASs; to mention a few, the observer-based distributed consensus protocol for MASs is proposed in [7]; the fault-tolerant consensus tracking problem for MASs with switching network is studied in [8]; the problem of timevarying formation control for MASs with time delay and switching topologies is investigated in [9].…”

Section: Introductionmentioning

confidence: 99%

$H_{\infty}$ Consensus Design and Online Scheduling for Multiagent Systems with Switching Topologies via Deep Reinforcement Learning

Cheng

Song

Xu³

et al. 2022

International Journal of Aerospace Engineering

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This paper is devoted to H ∞ consensus design and online scheduling for homogeneous multiagent systems (MASs) with switching topologies via deep reinforcement learning. The model of homogeneous MASs with switching topologies is established based on switched systems theory, in which the switching of topologies is viewed as the switching among subsystems. By employing linear transformation, the closed-loop systems of MASs are converted into reduced-order systems. The problem of H ∞ consensus design can be transformed to the issue of H ∞ control. It is supposed that the consensus protocol is composed of two parts: dynamics-based protocol and learning-based protocol, where dynamics-based protocol is provided to guarantee the convergence and weighted attenuation and learning-based protocol is proposed to improve the transient performance. Then, the multiple Lyapunov function (MLF) method and mode-dependent average dwell time (MDADT) method are combined to ensure the stability and weighted H ∞ disturbance attenuation index of reduced-order systems. The sufficient existing conditions of dynamics-based protocol are given through the feasible solutions of linear matrix inequalities (LMIs). Moreover, the online scheduling is formulated as a Markov decision process, and the deep deterministic policy gradient (DDPG) algorithm in the framework of actor-critic is proposed for the compensation of disturbance to explore optimal control policy. The online scheduling of parameters of MASs is viewed as bounded compensation of dynamics-based protocol, whose stability can be guaranteed by nonfragile control theory. Finally, simulation results are provided to illustrate the effectiveness and superiority of the proposed method.

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Time‐varying formation of second‐order discrete‐time multi‐agent systems under non‐uniform communication delays and switching topology with application to UAV formation flying

Cited by 33 publications

References 59 publications

A Leader‐Follower Formation Control of Multi‐UAVs via an Adaptive Hybrid Controller

A Leader‐Follower Formation Control of Multi‐UAVs via an Adaptive Hybrid Controller

Intelligent L2-L∞ Consensus of Multiagent Systems under Switching Topologies via Fuzzy Deep Q Learning

$H_{\infty}$ Consensus Design and Online Scheduling for Multiagent Systems with Switching Topologies via Deep Reinforcement Learning

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Time‐varying formation of second‐order discrete‐time multi‐agent systems under non‐uniform communication delays and switching topology with application to UAV formation flying

Cited by 33 publications

References 59 publications

A Leader‐Follower Formation Control of Multi‐UAVs via an Adaptive Hybrid Controller

A Leader‐Follower Formation Control of Multi‐UAVs via an Adaptive Hybrid Controller

Intelligent L2-L∞ Consensus of Multiagent Systems under Switching Topologies via Fuzzy Deep Q Learning

H ∞ Consensus Design and Online Scheduling for Multiagent Systems with Switching Topologies via Deep Reinforcement Learning

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$H_{\infty}$ Consensus Design and Online Scheduling for Multiagent Systems with Switching Topologies via Deep Reinforcement Learning