Robust Modular Control over Graph-Structured Feedback Architectures

This dissertation develops a unified framework for robust and modular feedback control of graph-structured cyber-physical systems (CPS) in which a human or mobile agent operates in the loop. In particular, we introduce a modular feedback synthesis based on linear matrix inequalities (LMIs) that guarantees closed-loop stability for all admissible feedback gains by sharing a common Lyapunov function. From a coalitional control viewpoint, the modular structure assigns a feedback block to each communication link, so that topology changes are handled by zeroing the blocks associated with disabled links, without redesign. To address scalability, the synthesis is combined with clustering-based partitioning, and the resulting trade-off between coordination effort and performance is analyzed against centralized, decentralized, and coalitional baselines. Secondly, we extend the framework to human-in-the-loop operation through a multi-scenario, tube-based model predictive control (MPC) scheme that uses the modular feedback as an ancillary control law. This design preserves constraint satisfaction and stability under actuator overrides by an operator, providing resilience to rapid and unpredictable interventions while avoiding frequent recomputation of feedback gains. Next, we formulate a feedback design for systems where an agent sequentially actuates subsystems along a periodic route on a graph. By casting the dynamics as a resampled system and selecting a periodic walk that maximizes the controllability of the resampled pair, we synthesize a stabilizing LMI feedback and compute a terminal maximal robust positively invariant set that ensures closed-loop stability within predictive control formulations. Overall, this thesis shows that modular feedback, tube-based MPC, and resampled-system invariants can be composed into a practical and scalable toolkit for robust control of networked CPS with operators in the loop.

Author dc:creator

• López Rodríguez, Francisco

Advisors dc:contributor.advisor

• Maestre Torreblanca, José María
• Muros Ponce, Francisco Javier

Statement dc:rights

• Attribution 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/11441/185771

OAI identifier oai:identifier

oai:idus.us.es:11441/185771