Reinforcement Learning for Multi-Agent Systems with an Application to Distributed Predictive Cruise Control

Mynuddin, Mohammed; Gao, Weinan; Jiang, Zhong Ping

doi:10.23919/acc45564.2020.9147968

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Compared with the centralized and decentralized control methods, the distributed control strategy based on multi-agent systems is a control protocol for all agents depending only on local information which has the advantages of not relying on a control center and can effectively save the costs of communication and computation. In recent years, the research on distributed multi-agent cooperative control has attracted much attention due to its wide applications in different areas, for example, microgrid (Peng et al, 2021; Xu et al, 2019), formation control (Gong et al, 2020; Siavash et al, 2020), intelligent transportation (Mynuddin et al, 2020), and distributed sensor fusion (Yan et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Consensus error analysis for linear continuous-time multi-agent systems with additive noises

Sun

Wang

Liu

2022

Transactions of the Institute of Measurement and Control

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This paper investigates the distributed consensus problem for linear continuous-time multi-agent systems subject to additive process noises. Due to the existence of noises, the asymptotic consensus cannot be achieved. Hence, a distributed consensus protocol is proposed such that all the agents can achieve consensus to some degree. Furthermore, based on Lyapunov theory, we characterize the consensus error under undirected and directed topologies. Numerical examples are presented to verify the effectiveness of two results.

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Section: Introductionmentioning

confidence: 99%

Consensus error analysis for linear continuous-time multi-agent systems with additive noises

Sun

Wang

Liu

2022

Transactions of the Institute of Measurement and Control

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Experimental Studies On Drivers Distractions: Investigating the Effects of Distractions on Driving Performance

Mynuddin,

Sadeeq,

Uddin Khan

et al. 2023

2023 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific)

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Driving hazards encompass a range of factors, such as poor road conditions, weather, and human behavior. They represent a significant threat to the safety of individuals on the road and are very likely one of the leading causes of traffic accidents. These hazards exist alongside well-known risk factors like driving under the influence of drugs, extreme fatigue, drowsiness, and excessive speeding. Impaired driving, which includes behaviors such as drunk or drugged driving, has persistently plagued American roads for many years. It poses an ongoing risk, not only to the impaired driver but also to other road users and the surrounding infrastructure. The primary objective of this paper is to investigate specific forms of distractions that drivers encounter. These distractions encompass activities such as texting or using a mobile device, eating, conversing with passengers, and paying attention to advertising signs along the roadside. The research aims to understand how each of these distractions affects a driver's ability to operate a vehicle safely. In essence, this chapter delves into the various ways in which these distractions can compromise a driver's attention and, consequently, impact road safety.

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Data-driven control of hydraulic servo actuator based on adaptive dynamic programming

Djordjevic

Stojanović

Tao

et al. 2022

DCDS-S

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<p style='text-indent:20px;'>The hydraulic servo actuators (HSA) are often used in the industry in tasks that request great powers, high accuracy and dynamic motion. It is well known that HSA is a highly complex nonlinear system, and that the system parameters cannot be accurately determined due to various uncertainties, inability to measure some parameters, and disturbances. This paper considers control problem of the HSA with unknown dynamics, based on adaptive dynamic programming via output feedback. Due to increasing practical application of the control algorithm, a linear discrete model of HSA is considered and an online learning data-driven controller is used, which is based on measured input and output data instead of unmeasurable states and unknown system parameters. Hence, the ADP based data-driven controller in this paper requires neither the knowledge of the HSA dynamics nor exosystem dynamics. The convergence of the ADP based control algorithm is also theoretically shown. Simulation results verify the feasibility and effectiveness of the proposed approach in solving the optimal control problem of HSA.</p>

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Reinforcement Learning for Multi-Agent Systems with an Application to Distributed Predictive Cruise Control

Cited by 3 publications

References 27 publications

Consensus error analysis for linear continuous-time multi-agent systems with additive noises

Consensus error analysis for linear continuous-time multi-agent systems with additive noises

Experimental Studies On Drivers Distractions: Investigating the Effects of Distractions on Driving Performance

Data-driven control of hydraulic servo actuator based on adaptive dynamic programming

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