An Algorithm of Servo Feedback Anomaly Detection and Suppression Based on Adaptive Confidence

In response to the problem that traditional feedback anomaly detection method based on fixed-threshold is prone to failure when facing continuous abnormal signal or changes in system states, an algorithm of servo feedback anomaly detection and suppression based on adaptive confidence is proposed in this paper. Firstly, through theoretical analysis, a control system model is established and feedback values are predicted according to the system state matrix and input signals. Then, a dynamic confidence evaluation framework based on the feedback error distribution is constructed to assess the credibility of feedback sampling values. Finally, through the confidence-weighted fusion strategy, the true feedback value of the system is obtained for subsequent feedback control. Simulation results show that the algorithm can effectively suppress abnormal feedback and significantly improve the system’s control performance under both short-term and long-term anomaly conditions; in the case of long-term abnormal feedback. Compared with traditional feedback anomaly detection algorithm, the algorithm proposed in this paper can effectively suppress the propagation of cumulative errors and improve the stability of the control system.