Using dynamic data reconciliation to improve the performance of PID feedback control systems with Gaussian/non-Gaussian distributed disturbance and measurement noise

For a stochastic PID feedback control system, the uncertainty of the working environment often leads to the unsatisfied performance of the system, which does not meet the profit requirements. The working environment generally includes external disturbance and measurement noise, etc. Gaussian distributed measurement noise and disturbances are widely considered while non-Gaussian distributed measurement noise and disturbances are rarely considered. In this paper, the performance degradation of Gaussian/non-Gaussian disturbances and measurement noise on a stochastic PID feedback system is considered and analyzed. An efficient method, dynamic data reconciliation (DDR) is developed to filter measurement noise and disturbances and improve the performance of the stochastic PID feedback control system. By utilizing model-based and measured information, DDR avoids time delays in output estimation. With the detailed theoretical analysis and simulation verification, the effectiveness of the proposed DDR technology on the stochastic PID feedback control system is verified. Compared with conventional exponential filters, DDR can achieve better control performance. The proposed DDR is also used for the control system of the DC-AC converter. The improved effect of DDR on the output quality is demonstrated by the results. & COPY; 2023 Published by Elsevier Ltd on behalf of ISA.

Automated summary

For a stochastic PID feedback control system, the uncertainty of the working environment often leads to unsatisfactory performance. This paper focuses on the performance degradation caused by Gaussian/non-Gaussian disturbances and measurement noise. A method called dynamic data reconciliation (DDR) is proposed to filter out noise and disturbances and improve the system's performance.