标题：Composite Learning Robot Control with Friction Compensation: A Neural Network-Based Approach
作者：Guo K.; Pan Y.; Yu H.
作者机构：[Guo, K] Mechanical Engineering, Shandong University, 12589 Jinan, Shandong China 250061 (e-mail: email@example.com);[ Pan, Y] Department of Biomedica 更多
来源：IEEE Transactions on Industrial Electronics
关键词：Adaptive control; Artificial neural networks; composite learning; Convergence; Friction; friction compensation; neural network approximation; robot manipulator; Service robots; Standards
摘要：Friction is one of the significant obstacles that hinders high-performance robot tracking control because accurate friction modeling and effective compensation are challenging issues. To address this problem, we propose a modified neural network (NN) structure with additional jump approximation activation functions to model the inherent discontinuous friction in robotic systems, this structure allows to improve the NN approximation accuracy without using too many NN nodes. The modeling accuracy is theoretically guaranteed by a composite learning technique, it explores both online historical data and instantaneous data to achieve NN weight convergence under a much weaker interval-excitation condition than the stringent persistent-excitation condition. Furthermore, a partitioned NN technique is used to handle a problem caused by variable substitution when formulating the prediction error for composite learning. This technique also helps to alleviate the requirements regarding the inertial matrix inversion and joint acceleration signals. The practical exponential stability of the closed-loop system is proved under the more realizable interval-excitation condition. Experimental results demonstrate the effectiveness and superiority of the proposed approach. IEEE