Selected work

Representative Papers

Coalitional model predictive control of an irrigation canal

Apr 01, 2014

Large-scale irrigation canal systems (e.g., the Dez Canal) face significant challenges in multi-gate coordinated regulation, including high communication overhead, stringent input constraints, slow state response, and weak disturbance rejection. To address these issues, this paper proposes a novel framework integrating coalition game theory with distributed model predictive control (MPC). It is the first work to embed a dynamic coalition formation mechanism into a distributed MPC architecture, enabling subsystems to autonomously form and dissolve coalitions based on real-time operating conditions—while respecting hard input constraints and balancing global optimization with local autonomy. Leveraging linear time-varying modeling, receding-horizon optimization, and explicit constraint handling, the method achieves coordinated water-level and flow control in representative canal system simulations: overshoot is reduced by 35%, settling time improves by 28%, and robustness and scalability are significantly enhanced.

101 citations2 influentialRead paper

Coalitional Control: Cooperative Game Theory and Control

Jan 18, 2017IEEE Control Systems

To address stability and cooperative optimization challenges in decentralized control of large-scale distributed systems, this work integrates cooperative game theory with control theory to establish, for the first time, a systematic framework of coalition control. It embeds game-theoretic solution concepts—such as the Shapley value and core—directly into feedback control law design, thereby unifying performance allocation and strategic coordination. Methodologically, the approach combines distributed model predictive control (MPC), multi-agent modeling, and robust stability analysis to develop a cooperative decision-making mechanism under partial information sharing. Experimental validation on power grid and traffic flow control benchmarks demonstrates a 23% improvement in cooperative efficiency over conventional decentralized control, alongside significantly enhanced robustness against disturbances and uncertainties. The core contribution is a novel, game-theoretic-solution-driven distributed control paradigm with provable closed-loop stability guarantees.

87 citations3 influentialRead paper

Coalitional control: A bottom-up approach

Jul 01, 2015American Control Conference

To address low collaboration efficiency and poor adaptability in cross-subteam coordination within multi-agent systems, this paper proposes a bottom-up dynamic cooperative control framework. The method dynamically evolves communication-driven control coalitions in real time based on time-varying inter-subsystem coupling strength and embeds cooperative game mechanisms into a distributed model predictive control (MPC) architecture to jointly optimize coalition structure and controller parameters. Its key innovation lies in establishing, for the first time, a coupling-strength-guided coalition formation mechanism that simultaneously enhances coordination efficiency and robustness under competitive environments. Evaluation on canonical coupled systems demonstrates that the proposed approach significantly reduces overall control cost, improves response consistency and disturbance rejection capability, and adaptively adjusts communication overhead according to coupling degree—outperforming fixed-topology or static-coalition strategies.

6 citationsRead paper

Safe and Efficient Social Navigation through Explainable Safety Regions Based on Topological Features

Feb 14, 2025arXiv.org

This work addresses the critical challenge of safe, efficient, and interpretable navigation for autonomous robots in human-robot cohabited social environments under dynamic conditions. We propose the first topological data analysis (TDA)-based safety region modeling framework: TDA extracts robust topological features of the environment to construct explainable safety regions with rigorously bounded approximation error (ε). We further introduce a novel safety boundary generation method integrating global support vector machines (SVM) with sequential statistics, ensuring deadlock-free navigation. Additionally, we extract locally interpretable decision rules to jointly guarantee robustness and transparency. Experiments demonstrate that our approach significantly improves collision detection accuracy while simultaneously enhancing behavioral predictability and human trust in compliant simulation environments.

1 citationsRead paper

Diameter and Length of Metric Graphs

Aug 09, 2026

This study investigates the relationship between the total length of a metric graph and its diameter, number of leaves, and cyclomatic number, aiming to establish a sharp upper bound. By integrating tools from metric space theory, graph-theoretic analysis of cyclomatic number and leaf count, geometric constructions, and extremal graph classification, the authors prove that the total length satisfies the inequality \( \text{len}(G) \leq (\text{cyc}(G) + \max\{1, \ell(G)/2\}) \cdot \text{diam}(G) \). This bound is the first tight upper bound that simultaneously incorporates both the number of leaves and the cyclomatic number. Moreover, the class of extremal graphs achieving equality is fully characterized: these are precisely the metric graphs obtained by identifying vertices of cycles or star graphs.

0 citationsRead paper

Recent publications

Latest Papers

Diameter and Length of Metric Graphs

Aug 09, 2026

This study investigates the relationship between the total length of a metric graph and its diameter, number of leaves, and cyclomatic number, aiming to establish a sharp upper bound. By integrating tools from metric space theory, graph-theoretic analysis of cyclomatic number and leaf count, geometric constructions, and extremal graph classification, the authors prove that the total length satisfies the inequality \( \text{len}(G) \leq (\text{cyc}(G) + \max\{1, \ell(G)/2\}) \cdot \text{diam}(G) \). This bound is the first tight upper bound that simultaneously incorporates both the number of leaves and the cyclomatic number. Moreover, the class of extremal graphs achieving equality is fully characterized: these are precisely the metric graphs obtained by identifying vertices of cycles or star graphs.

0 citationsRead paper

Train Small, Deploy Large: Zero-Shot GNN Transfer Through Geometric Renormalization

Jul 30, 2026

This work addresses the high computational cost and strong hardware demands of training graph neural networks (GNNs) on large-scale graphs by proposing a zero-shot cross-scale transfer method based on geometric renormalization. The approach first coarsens the original graph into a smaller-scale surrogate, trains a GNN on this compact representation, and then directly transfers the learned weights back to the original graph without any fine-tuning. This is the first method to enable cross-scale GNN deployment without retraining, revealing that structural similarity—not graph size—is the dominant factor in predictive performance. The framework establishes a new paradigm toward scale-equivariant GNNs. Experiments on both synthetic and real-world networks demonstrate that the method drastically reduces training overhead while achieving prediction accuracy comparable to full-scale training, with highly consistent cross-scale representations and prediction trajectories.

0 citationsRead paper

A Taxonomy of Performance Metrics for the Distributed Computing Continuum

Jul 30, 2026

This work addresses the limitations of existing performance evaluation approaches for distributed computing continua, which often focus on a single dimension and fail to holistically characterize the behavior of cross-layer heterogeneous systems. The paper presents the first systematic framework that establishes a comprehensive taxonomy of performance metrics spanning three layers—computation, networking, and application/user—as well as emerging non-functional attributes such as sustainability and observability. By integrating mathematical modeling with cross-layer analysis, the study rigorously defines the applicability, measurement phases, and specifications for each metric category. The resulting framework is both clearly structured and extensible, offering a solid theoretical foundation and practical guidance for unified performance assessment in dynamic, heterogeneous environments.

0 citationsRead paper

Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detection

Jul 14, 2026

This work addresses the limitations of traditional multivariate time series anomaly detection methods, which rely on task-specific models with high training costs and limited generalization. It presents the first systematic evaluation of the zero-shot applicability of TimesFM—a pretrained univariate foundation model—to industrial-scale multivariate anomaly detection. Two strategies are proposed: using TimesFM as a per-feature predictor with prediction-error-based thresholds, or leveraging its intermediate representations as embeddings fed into standard outlier detection algorithms. Experimental results show that, while TimesFM does not consistently outperform existing baselines—due to overfitting to temporal dynamics that obscures persistent anomalies—it exhibits pronounced error peaks at anomaly onset and offset points and demonstrates high sensitivity to distributional shifts, revealing promising potential for change-point detection.

0 citationsRead paper

Safe Reinforcement Learning using Ideas from Model Predictive Control

Jul 08, 2026

This work addresses the challenge that reinforcement learning often fails to strictly satisfy hard safety constraints during exploration, risking irreversible damage to physical systems. To overcome this limitation, the authors propose a general safety framework that integrates deep reinforcement learning (DRL) with model predictive control (MPC). The approach leverages a known system dynamics model to construct an offline feasible set of state-action pairs and employs an online safety filter that projects the agent’s actions in real time onto this set, thereby providing formal safety guarantees throughout both training and deployment. By combining the safety assurances of MPC with the adaptive capabilities of DRL, the method enables safe exploration, stable policy convergence, and hardware protection, as demonstrated on a nonlinear single-degree-of-freedom experimental platform.

0 citationsRead paper