Two-Tank Water System Dataset for RLS-Based Identification and Adaptive PID Control Under Diverse Operating Conditions

This dataset contains 25 long-duration time-series experiments collected from a laboratory two-tank water pumping plant. The experiments were performed under three operating conditions: Steady-State Baseline (Minimal Outflow) Steady-State With Constant Passive Outflow Dynamic Disturbance Rejection (High and Low Outflow Transients) Each experiment lasts approximately 8.5 hours, sampled at 1 Hz, and is provided as an individual CSV file. The dataset includes raw process measurements, control signals, reference setpoints, adaptive PID controller gains, and real-time model parameters estimated using a Recursive Least Squares (RLS) algorithm. Disturbance experiments include an additional binary flag indicating externally induced outflow changes. This dataset serves as a real world benchmark for system identification, adaptive control, disturbance rejection, and anomaly detection, capturing essential nonlinearities such as turbulence, actuator saturation, and abrupt hydraulic transitions. Acknowledgement This research is the result of the Strategic Project "Critical infrastructures cybersecure through intelligent modeling of attacks, vulnerabilities and increased security of their IoT devices for the water supply sector'' (Ref. C061/23), formed under the collaboration agreement between the National Institute of Cybersecurity (INCIBE) and the University of A Coruña. This initiative is carried out within the framework of the Recovery, Transformation and Resilience Plan funds, financed by the European Union (Next Generation).