Stability of a Class of Multiagent Tracking Systems With Unstable Subsystems

Dong, Lijing; Chai, Senchun; Zhang, Baihai; Nguang, Sing Kiong; Chai, Runqi

doi:10.1109/tcyb.2016.2581830

Cited by 21 publications

(5 citation statements)

References 44 publications

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“…To that end, we have integrated ANN into our model in order to examine nonlinear relationship among features. Advanced computing and AI can provide manufacturing with a higher degree of intelligence and low-cost sensing and improve efficiency [33]. The process of conducting intelligent manufacturing can be regarded in two ways.…”

Section: Related Workmentioning

confidence: 99%

AI-based modeling and data-driven evaluation for smart manufacturing processes

Ghahramani

Qiao

Zhou

et al. 2020

IEEE/CAA J. Autom. Sinica

229

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Smart Manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying Industrial Internet of Things (IIoT) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management. Embracing Machine Learning and Artificial Intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on Evolutionary Computing and Deep Learning algorithms toward making semiconductor manufacturing smart. We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes and to address various challenges. We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm. Our objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.

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Section: Related Workmentioning

confidence: 99%

AI-based modeling and data-driven evaluation for smart manufacturing processes

Ghahramani

Qiao

Zhou

et al. 2020

IEEE/CAA J. Autom. Sinica

229

View full text Add to dashboard Cite

show abstract

“…In the multi-agent tracking scenario, agents in different positions aim to be consistent with the target agent through learning, which can often be represented as a set of different agents with an inherent high-level structure [42]. Every single agent could be abstracted as a node.…”

Section: Background 21 Graph Theorymentioning

confidence: 99%

Multi-Agent Distributed Deep Deterministic Policy Gradient for Partially Observable Tracking

2021

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In many existing multi-agent reinforcement learning tasks, each agent observes all the other agents from its own perspective. In addition, the training process is centralized, namely the critic of each agent can access the policies of all the agents. This scheme has certain limitations since every single agent can only obtain the information of its neighbor agents due to the communication range in practical applications. Therefore, in this paper, a multi-agent distributed deep deterministic policy gradient (MAD3PG) approach is presented with decentralized actors and distributed critics to realize multi-agent distributed tracking. The distinguishing feature of the proposed framework is that we adopted the multi-agent distributed training with decentralized execution, where each critic only takes the agent’s and the neighbor agents’ policies into account. Experiments were conducted in the distributed tracking tasks based on multi-agent particle environments where N(N=3,N=5) agents track a target agent with partial observation. The results showed that the proposed method achieves a higher reward with a shorter training time compared to other methods, including MADDPG, DDPG, PPO, and DQN. The proposed novel method leads to a more efficient and effective multi-agent tracking.

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“…The complexity is significantly increased if there are unstable subsystems in multi-agent switching systems [34]. The work in [35] focused on a class of cyclic switched nonlinear systems, and achieved the finite-time stability by making the most of the trade-off among impulsive dynamics, initial conditions, and the dynamics of each mode.…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis of Multi-Agent Tracking Systems with Quasi-Cyclic Switching Topologies

2020

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In this paper, the stability problem of a class of multi-agent tracking systems with quasi-cyclic switching topologies is investigated. The existing results of systems with switching topologies are usually achieved based on the assumption that the piecewise constant communication topologies are connected and the switchings are cyclic. The communication topologies are possible to be unconnected and it is difficult to guarantee the topologies switch circularly. The piecewise unconnected topology makes the interactive multi-agent tracking system to be an unstable subsystem over this time interval. In order to relax the assumption constraint, a quasi-cyclic method is proposed, which allows the topologies of multi-agent systems to switch in a less conservative way. Moreover, the stability of the tracking system with the existence of unstable subsystems is analyzed based on switched control theory. It is obtained that the convergence rate is affected by the maximum dwell time of unstable subsystems. Finally, a numerical example is provided to demonstrate the effectiveness of the theoretical results.

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Stability of a Class of Multiagent Tracking Systems With Unstable Subsystems

Cited by 21 publications

References 44 publications

AI-based modeling and data-driven evaluation for smart manufacturing processes

AI-based modeling and data-driven evaluation for smart manufacturing processes

Multi-Agent Distributed Deep Deterministic Policy Gradient for Partially Observable Tracking

Stability Analysis of Multi-Agent Tracking Systems with Quasi-Cyclic Switching Topologies

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