Decentralized Consensus Optimization and Resource Allocation

Nedić, Angelia; Оlshevsky, А.G.; Shi, Wei

doi:10.1007/978-3-319-97478-1_10

Cited by 15 publications

(17 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors of [40] developed a novel consensus approach called Gaussian Mixture-Cardinalized Probability Hypothesis Density (GM-CPHD) filter for multitarget tracking application. Despite many distributed fusion approaches, some consensus approaches [41][42][43][44][45] are very successful in homogeneous data fusion due to the scalability requirement, the lack of a fusion center, and limited knowledge of the whole sensor network (more details in [46]). The authors of [46] surveyed both classical approaches and recent advances in multi-sensor data fusion for sensor networks.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

UAV Formation Shape Control via Decentralized Markov Decision Processes

2021

View full text Add to dashboard Cite

In this paper, we present a decentralized unmanned aerial vehicle (UAV) swarm formation control approach based on a decision theoretic approach. Specifically, we pose the UAV swarm motion control problem as a decentralized Markov decision process (Dec-MDP). Here, the goal is to drive the UAV swarm from an initial geographical region to another geographical region where the swarm must form a three-dimensional shape (e.g., surface of a sphere). As most decision-theoretic formulations suffer from the curse of dimensionality, we adapt an existing fast approximate dynamic programming method called nominal belief-state optimization (NBO) to approximately solve the formation control problem. We perform numerical studies in MATLAB to validate the performance of the above control algorithms.

show abstract

Section: Introductionmentioning

confidence: 99%

“…In [52], the authors developed a distributed consensus tracking filter to solve the target tracking problem. The authors in [45] discussed algorithms for solving decentralized consensus optimization problems.…”

Section: Introductionmentioning

confidence: 99%

UAV Formation Shape Control via Decentralized Markov Decision Processes

2021

View full text Add to dashboard Cite

show abstract

“…We should point out that, for the sake of brevity, our survey of previous work here is short of complete and only contains the previous work related to our work. For a more complete survey we refer to [22], [26], [31] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Distributed Constrained Consensus of Utilities via a Self Evaluation Approach

Wang

Sun

2019

IEEE Access

View full text Add to dashboard Cite

This paper investigates the problem of distributed constrained consensus of utilities for multiple networked nodes, which is essentially challenging in view of the fact that local constraints may hinder the global optimization across the network. Focusing on this problem, a generalized framework is developed via a self evaluation approach, in which each node uses utility evaluations to update individual states by interacting with neighbors only. Following the framework, the distributed constrained consensus algorithms is derived, with theoretical analyses presented to show the convergence and stability. In addition, an approximation method is proposed as well for ease of engineering implementation. Finally, illustrative examples are provided and analyzed to substantiate the efficacy of the proposed algorithm.

show abstract

“…We remark that the notions of distributed and decentralized computation are not unified in the literature. Here, we use the terminology from the optimization community[12],[13], where distributed computation allows a small amount of central coordination activity and decentralized computation avoids central coordination and relies on neighbor-to-neighbor communication only. We remark that in the control systems context, decentralized algorithms do not allow for any communication exchange[11],[14], while algorithms comprising a coordinator are denoted as hierarchical.…”

mentioning

confidence: 99%

Distributed Control of Charging for Electric Vehicle Fleets Under Dynamic Transformer Ratings

Botkin-Levy

Engelmann

Mühlpfordt

et al. 2022

IEEE Trans. Contr. Syst. Technol.

View full text Add to dashboard Cite

Due to their large power draws and increasing adoption rates, electric vehicles (EVs) will become a significant challenge for electric distribution grids. However, with proper charging control strategies, the challenge can be mitigated without the need for expensive grid reinforcements. This article presents and analyzes new distributed charging control methods to coordinate EV charging under nonlinear transformer temperature ratings. Specifically, we assess the tradeoffs between required data communications, computational efficiency, and optimality guarantees for different control strategies based on a convex relaxation of the underlying nonlinear transformer temperature dynamics. Classical distributed control methods, such as those based on dual decomposition and alternating direction method of multipliers (ADMM), are compared against the new augmented Lagrangian-based alternating direction inexact Newton (ALADIN) method and a novel low-information, lookahead version of packetized energy management (PEM). These algorithms are implemented and analyzed for two case studies on residential and commercial EV fleets with fixed and variable populations. The latter motivates a novel EV hub charging model that captures arrivals and departures. Simulation results validate the new methods and provide insights into key tradeoffs.

show abstract

Decentralized Consensus Optimization and Resource Allocation

Cited by 15 publications

References 56 publications

UAV Formation Shape Control via Decentralized Markov Decision Processes

UAV Formation Shape Control via Decentralized Markov Decision Processes

Distributed Constrained Consensus of Utilities via a Self Evaluation Approach

Distributed Control of Charging for Electric Vehicle Fleets Under Dynamic Transformer Ratings

Contact Info

Product

Resources

About