Neural Network Based Contact Force Control Algorithm for Walking Robots

Abstract

Walking algorithms using push-off improve moving efficiency and disturbance rejection performance. However, the algorithm based on classical contact force control requires an exact model or a Force/Torque sensor. This paper proposes a novel contact force control algorithm based on neural networks. The proposed model is adapted to a linear quadratic regulator for position control and balance. The results demonstrate that this neural network-based model can accurately generate force and effectively reduce errors without requiring a sensor. The effectiveness of the algorithm is assessed with the realistic test model. Compared to the Jacobian-based calculation, our algorithm significantly improves the accuracy of the force control. One step simulation was used to analyze the robustness of the algorithm. In summary, this walking control algorithm generates a push-off force with precision and enables it to reject disturbance rapidly.

Constant force control for aluminum wheel hub grinding based on ESO + backstepping

“…Zhang and Liang (2019) proposed a hybrid sliding mode impedance control method based on disturbance observer and the experimental results showed that the force tracking performance and robustness of the system were significantly improved. Kim and Kim (2021) proposed a force control method based on neural network in order to suppress unknown interference in the contact force control process and reduce modeling error. The results showed that this method has high applicability to contact force control, but the response time is slow.…”