Data-Driven Predictive Control (DDPC) has emerged as a promising alternative to Model Based Control (MBC), enabling direct control using Input-Output (I/O) data without requiring explicit model identification. This thesis advances DDPC by bridging the gap between theoretical developments and real-world implementation. The study focuses on four algorithmic variants: Subspace Predictive Control (SPC), Closed Loop Subspace Predictive Control (CL SPC), Recursive Closed Loop Subspace Predictive Control (R-CL SPC), and Constrained Recursive Closed Loop Subspace Predictive Control (CR-CL SPC), to enhance adaptability and ensure real-time feasibility.

A key insight in computational efficiency is the R-CL SPC algorithm, which updates system parameters online using recursive least squares estimation combined with Givens rotations. This reduces computational complexity by avoiding large-scale matrix inversions at each time step. Additionally, the CR-CL SPC variant introduces constraint handling via a Quadratic Programming (QP) solver, enabling input and output constraints.

These algorithms’ performances were evaluated through simulation studies and real-time experiments on a piezo-actuated beam setup, where the control objective was to suppress the first two natural vibration modes. The R-CL SPC algorithm demonstrated a strong balance between computational efficiency and control performance, achieving execution times below 0.2 ms while maintaining effective vibration damping. Meanwhile, though at a higher computational cost, CR-CL SPC validated constraint enforcement capabilities.

This thesis demonstrates that adaptive SPC algorithms can be successfully implemented on real-world hardware. The results contribute to the growing knowledge on direct DDPC strategies and provide a foundation for their broader application in real-time, constrained control systems.