Adaptive output-feedback bipartite consensus for nonstrict-feedback nonlinear multi-agent systems: A finite-time approach

Shahvali, Milad; Sistani, Mohammad Bagher Naghibi; Askari, Javad

doi:10.1016/j.neucom.2018.07.039

Cited by 51 publications

(27 citation statements)

References 36 publications

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“…Moreover, when dealing with non-adversarial multiple agents, global planners focus on the consensus of such agents. In [40], [41], the author builds a global controller with the objective of cooperation among the agents which compose the systems, in a latter publication [42], instead of fully modelling the dynamics of the system, the uncertainties were dealt with an adaptative neural network.…”

Section: A Related Workmentioning

confidence: 99%

Temporal Graph Traversals Using Reinforcement Learning With Proximal Policy Optimization

2020

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Graphs in real-world applications are dynamic both in terms of structures and inputs. Information discovery in such networks, which present dense and deeply connected patterns locally and sparsity globally can be time consuming and computationally costly. In this paper we address the shortest path query in spatio-temporal graphs which is a fundamental graph problem with numerous applications. In spatiotemporal graphs, shortest path query classical algorithms are insufficient or even flawed because information consistency can not be guaranteed between two timestamps and path recalculation is computationally costly. In this work, we address the complexity and dynamicity of the shortest path query in spatio-temporal graphs with a simple, yet effective model based on Reinforcement Learning with Proximal Policy Optimization. Our solution simplifies the problem by decomposing the spatio-temporal graph in two components: a static and a dynamic sub-graph. The static graph, known and immutable, is efficiently solved with A * algorithm. The sub-graphs interconnecting the static graph have unknown dynamics and we address such issue by estimating the unknown dynamic portion of the graph as a Markov Chain which correlates the observations of the agents in the environment and the path to be followed. We then derive an action policy through Proximal Policy Optimization to select the local optimal actions in the Markov Process that will lead to the shortest path, given the estimated system dynamics. We evaluate the system in a simulation environment constructed in Unity3D. In partially structured and unknown environments, with variable environment parameters we've obtained an efficiency 75% greater than the comparable deterministic solution.

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Section: A Related Workmentioning

confidence: 99%

Temporal Graph Traversals Using Reinforcement Learning With Proximal Policy Optimization

2020

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“…The containment control problem is investigated for nonlinear strict‐feedback MASs with immeasurable states in Reference 49. A distributed adaptive output feedback tracking control solution for nonstrict‐feedback nonlinear MASs with unmeasurable states is developed in References 50,51. On the other hand, the input saturation is inevitable, because of the physical limitations in some practical MASs and its ignorance can cause performance deterioration or even instability.…”

Section: Introductionmentioning

confidence: 99%

Neuro‐adaptive distributed output‐feedback containment control for multiagent systems with nonstrict‐feedback nonlinear dynamics and input constraints

Peydayesh

Arefi

2021

Intl J Robust & Nonlinear

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In this article, distributed adaptive controllers are designed for containment control of multiagent systems (MASs) with high-order nonlinear dynamics in nonstrict-feedback form. To broaden the application horizon, the agents' dynamics are considered heterogeneous, and the control saturation is taken into account. Moreover, the agents' dynamics, as well as their full state information, are assumed unknown. An adaptive observer is designed in the first step for each agent to estimate the agents' states using available output measurements. An adaptive backstepping controller is then integrated with the observer to solve the containment control problem. The computational complexity of the controller is reduced by adopting the command filtered adaptive backstepping approach to the containment control of MASs. The effect of the input saturation is also compensated by employing an auxiliary system throughout the design procedure.The proposed control approach guarantees that the states of all agents are cooperatively semiglobally uniformly ultimately bounded and containment errors converge to a small neighborhood of the origin. Finally, the effectiveness of the presented control algorithm is confirmed by a simulation example.

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“…In particular, there has been an increasing interest in the control engineering community to develop effective distributed strategies to achieve some important global objectives in MASs such as consensus, formation, flocking and rendezvous [6–8]. In particular, consensus is a fundamental problem in a MAS which has attracted considerable attention due to its widespread applications [9, 10]. Different techniques are proposed in the literature for the consensus control of a MAS for agents with linear or non‐linear dynamics [11–17].…”

Section: Introductionmentioning

confidence: 99%

Distributed adaptive consensus tracking control for non‐linear multi‐agent systems with time‐varying delays

Zamani

Askari

Kamali

et al. 2020

IET Control Theory & Appl

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In this study, a novel distributed adaptive controller is provided for consensus control of high‐order non‐linear multi‐agent systems with unknown time‐varying delays. The system is subject to uncertain disturbances, and the agents' dynamics are not known. Unlike the existing literature, the proposed method does not require time‐delay terms in system dynamics to be bounded. A neural network is used to model the unknown non‐linear dynamics. Then, despite the destabilising effect of the unknown delays, some adaptive rules based on the dynamic surface control are designed to achieve the consensus objective. The semi‐global uniform boundedness of the resultant closed‐loop signals and the convergence of the tracking errors to a neighbourhood of the origin are shown mathematically. Simulations verify the effectiveness of the results.

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Adaptive output-feedback bipartite consensus for nonstrict-feedback nonlinear multi-agent systems: A finite-time approach

Cited by 51 publications

References 36 publications

Temporal Graph Traversals Using Reinforcement Learning With Proximal Policy Optimization

Temporal Graph Traversals Using Reinforcement Learning With Proximal Policy Optimization

Neuro‐adaptive distributed output‐feedback containment control for multiagent systems with nonstrict‐feedback nonlinear dynamics and input constraints

Distributed adaptive consensus tracking control for non‐linear multi‐agent systems with time‐varying delays

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