Control of spatially distributed systems is a challenging problem because of their complex nature, nonlinearity, and generally high order. The... Show moreControl of spatially distributed systems is a challenging problem because of their complex nature, nonlinearity, and generally high order. The lack of accurate and computationally efficient model-based techniques for large, spatially distributed systems leads to challenges in controlling the system. Agent-based control structures provide a powerful tool to manago distributed systems by utilizing (organizing) local and global information obtained from the system. A hierarchical, agent-based system with local and global controller agents is developed to control networks of interconnected chemical reactors (CSTRs). The global controller agent dynamically updates local controller agent's objectives as the reactor network conditions change. One challenge posed is control of the spatial distribution of autocatalytic species in a network of reactors hosting multiple species. The multi-agent control system is able to intelligently manipulate the network flow rates such that the desired spatial distribution of species is achieved. Furthermore, the robustness and flexibility of the agent-based control system is illustrated through examples of disturbance rejection and scalability with respect to the size of the network. Endnote format citation for DOI:10.1016/j.jprocont.2006.06.008 Show less
Supervision of distributed manufacturing processes producing different grades of a product requires intelligent reconfiguration strategies... Show moreSupervision of distributed manufacturing processes producing different grades of a product requires intelligent reconfiguration strategies during grade transition phases to minimize off-spec production. Agent-based approaches are ideal for such problems and they provide flexible, robust, and emergent solutions during dynamically changing process conditions. Three different multi-layered multi-agent, frameworks are proposed for the supervision of grade transitions in autocatalytic reactor networks. The first framework is the centralized framework and it is useful for small-scale grade transitions where only a small region of the network needs to be reconfigured. Alternatively, the other two frameworks use a decentralized approach. The first decentralized framework implements genetic algorithms and the second one uses self-organizing heuristics and auctions for large-scale grade transitions. The case studies demonstrate that as the complexity of the reconfiguration problem increases, decentralized solutions perform more efficiently. Endnote format citation for DOI:10.1016/j.compchemeng.2008.02.008 Show less
