Design of an LMI‐Based Direct Data‐Driven Controller for Reference Tracking With Experimental Validation

ABSTRACT This work newly proposes a direct data‐driven, linear matrix inequality (LMI)‐based reference tracking proportional‐integral (PI) controller for a marginally stable single‐link flexible system. The inherent flexibility of the link, coupled with unmodeled system dynamics, poses significant challenges in achieving accurate trajectory tracking and minimizing tip deflection. The system is first stabilized by a direct data‐driven state feedback controller (DDSFC). To enhance tracking performance, the DDSFC is complemented with the proposed PI reference tracking controller. This design is compared with a data‐driven neural network‐based proportional‐integral‐derivative (NNPID) controller. Experimental findings confirm that the proposed data‐driven control framework effectively captures real‐world dynamics. The resilience of the controller is further demonstrated under false data injection (FDI) and denial‐of‐service (DoS) attacks, validating its robustness and practical suitability for real‐time control applications. To the best of our knowledge, this problem has not been addressed in the existing literature. The present work aims to bridge the gap between control theory and practical implementation.