Robust Predictive Control for NARX Models from Input-Output Data

This paper considers the problem of designing predictive control laws for nonlinear auto-regressive exogenous (NARX) models based on measured input-output data without explicitly identifying the model parameters. We explore the case when outputs are corrupted by additive measurement noise. An upper bound on the optimal value function for the robust case is derived, and the mismatch between the predicted and actual output is also theoretically studied. The recursive feasibility and practical stability of the robust data-driven predictive control (DDPC) scheme are guaranteed. The simulation results finally quantify the Effectiveness of the proposed method with the experimental data gathered from a powder compaction process performed on a rotary tablet press.