Composite Learning Robot Control With Friction Compensation: A Neural Network-Based Approach

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, in this paper, 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 us 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 us 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.