Configuration of Model Error Compensator for Aperiodic Sampled-data Systems

Abstract

Control performance often deteriorates when conventional methods based on constant sampling intervals are applied to systems with varying sampling intervals, due to the mismatch between assumed and actual sampling characteristics. To address this challenge, we propose a novel design method for a Model Error Compensator (MEC) that mitigates the adverse effects caused by sampling interval variations. The MEC dynamically compensates for changes in the input-output characteristics of the controlled system, which occur due to aperiodic sampling, thereby enhancing the robustness and effectiveness of control methods designed for constant sampling intervals. Numerical simulations demonstrate that the proposed MEC significantly improves control performance in systems with aperiodic sampling by reducing model errors. Compared to conventional control approaches, the proposed method offers superior performance and contributes to the broader field of sampled-data system control by addressing the practical challenges associated with sampling variability.