Gangshan Jing

multi-agent systems, formation control, reinforcement learning, network localization

Works (6)

Updated: July 5th, 2023 14:42

2022 journal article

Fuel-Optimal Guidance for End-to-End Human-Mars Entry, Powered-Descent, and Landing Mission

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 58(4), 2837–2854.

By: C. Wan*, G. Jing n, R. Dai* & J. Rea*

author keywords: Convex functions; Optimal control; Convergence; Computational modeling; Aerodynamics; Programming; Mars; Alternating direction method of multipliers (ADMM); human-Mars entry; powered-descent; and landing (EDL); trajectory optimization
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy (OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: October 24, 2022

2022 journal article

Learning Distributed Stabilizing Controllers for Multi-Agent Systems

IEEE CONTROL SYSTEMS LETTERS, 6, 301–306.

author keywords: Heuristic algorithms; Multi-agent systems; Power system dynamics; Approximation algorithms; Damping; System dynamics; Regulators; Reinforcement learning; linear quadratic regulator; optimal distributed control; multi-agent systems
TL;DR: This work addresses model-free distributed stabilization of heterogeneous continuous-time linear multi-agent systems using reinforcement learning (RL) and builds upon the results of the first algorithm, and extends it to distributed stabilized systems with predefined interaction graphs. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: July 19, 2021

2021 article

Decomposability and Parallel Computation of Multi-Agent LQR

2021 AMERICAN CONTROL CONFERENCE (ACC), pp. 4527–4532.

By: G. Jing n, H. Bai*, J. George* & A. Chakrabortty n

author keywords: Reinforcement learning; linear quadratic regulator; multi-agent systems; decomposition
TL;DR: This work proposes a parallel RL scheme for a linear quadratic regulator (LQR) design in a continuous-time linear MAS and shows that if the MAS is homogeneous then this decomposition retains closed-loop optimality. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: November 1, 2021

2021 article

Local Shape-Preserving Formation Maneuver Control of Multi-agent Systems: From 2D to 3D

2021 60TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), pp. 6251–6257.

By: C. Wan*, G. Jing n, R. Dai* & R. Zhao*

author keywords: Formation control; rigidity theory; multi-agent systems
TL;DR: This paper proposes a formation maneuver control strategy to steer a triangulated formation from two dimensional space to three dimensional space, while maintaining the shape of each triangle during the transition, using a weak rigidity function containing both distance and angle constraints. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries
Added: May 31, 2022

2021 journal article

Model-Free Optimal Control of Linear Multiagent Systems via Decomposition and Hierarchical Approximation

IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS, 8(3), 1069–1081.

By: G. Jing n, H. Bai*, J. George* & A. Chakrabortty n

author keywords: Decomposition; hierarchical control; large-scale networks; linear quadratic regulator (LQR); model-free reinforcement learning (RL)
TL;DR: A hierarchical controller is proposed that adheres to a particular structure as specified by the inter-agent coupling in the objective function and by the decomposition strategy, and mathematical formulations are established to find a decomposition that minimizes required communication links or reduces the optimality gap. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: October 4, 2021

2020 journal article

Model-Free Reinforcement Learning of Minimal-Cost Variance Control

IEEE CONTROL SYSTEMS LETTERS, 4(4), 916–921.

By: G. Jing n, H. Bai*, J. George* & A. Chakrabortty n

author keywords: Mathematical model; Heuristic algorithms; Stochastic processes; Iterative methods; Riccati equations; Computational complexity; Optimal control; Reinforcement learning; stochastic dynamic systems; variance control; coupled Riccati equations
TL;DR: Two reinforcement learning algorithms for solving a class of coupled algebraic Riccati equations for linear stochastic dynamic systems with unknown state and input matrices where the input matrix can be estimated at the very first iteration are proposed. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: July 13, 2020

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