New approach of friction model and identification for hydraulic system based on MAPSO-NMDS optimization Elman neural network

Hydraulic system smoothness is critical to product quality and position tracking. However, the nonlinear friction is an important factor causing unstable phenomenon of the hydraulic system when at low speed. Relevant research shows that the identified nonlinear friction has a large error with the actual friction force, which will bring a great challenge to design the friction compensation controller. Considering the difficulty of the friction model and identification, this article presents a new approach of friction model and identification for hydraulic system based on multi-agent particle swarm optimization-Nelder-Mead downhill simplex algorithm optimization Elman neural network. First, the nonlinear friction was established using the Elman neural network model. Then, a multi-agent particle swarm optimization-Nelder-Mead downhill simplex algorithm was proposed to search the nonlinear parameters of the Elman neural network model. Subsequently, the proposed algorithm was validated by complex nonlinear functions. Finally, the effectiveness of the Elman neural network model was demonstrated by the results from experiment. And comparison with the conventional LuGre friction model, the results show that the Elman neural network model predicts friction more accurately. This study provides the bases for designing the friction compensation controller.