A dual-loop active vibration control technology with an RBF-RLS adaptive algorithm

Active control strategies have been widely used in micro-vibration isolation with higher perfor-mance requirements. Aiming at the problem of insufficient vibration isolation performance due to time delay in feedback control, the ground-based feedforward active control is introduced to compensate in advance for higher pursuit. However, the inaccuracy of the feedforward control reference model severely restricts the vibration isolation performance. With the assistance of RBF neural network algorithm for accurate real-time identification of online model, a feedforward-feedback dual-loop active hybrid control (DAHC) strategy based on the RBF-RLS adaptive algo-rithm is proposed in this paper. The adaptive feedforward control uses the RBF neural network algorithm to identify the accurate model of the system online, and input it to the transverse filter of the RLS adaptive control algorithm for recursive calculation, which can effectively reduce the system error caused by the inaccuracy of the model. The experimental results show that the accuracy of online model affects the amplitude attenuation performance by 57.1%. DAHC strategy can greatly reduce the resonance peak by 25.02 dB in micro-vibration. Real applicative experiment further proves the effectiveness of our proposed algorithm.

Automated summary

In this study, a feedforward-feedback dual-loop active hybrid control (DAHC) strategy based on the RBF-RLS adaptive algorithm is proposed. With the assistance of the RBF neural network algorithm, the accurate online model can be identified to effectively reduce the system error caused by model inaccuracy. Experimental results show that the accuracy of the online model affects the amplitude attenuation performance by 57.1%. Real applicative experiment further proves the effectiveness of the proposed algorithm.