Coalitional model predictive control of an irrigation canal

2014-04-01

AI Summary

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.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Multiagent Systems: Coordination and Collaboration
• Game Theory and Economic Paradigms: Cooperative Game Theory

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Problem

• Network Structure Optimization
• Control Efficiency
• Communication Cost

Innovation

• Distributed MPC
• Networked System Control
• Collaborative Forecasting