Adaptive Neural Network Tracking Control for Robotic Manipulators With Dead Zone
By Qi Zhou, Shiyi Zhao, Hongyi Li, Renquan Lu, Chengwei Wu. IEEE Transactions on Neural Networks and Learning Systems, 2019
321 citations recorded by the source corpus.
In this paper, the adaptive neural network (NN) tracking control problem is addressed for robot manipulators subject to dead-zone input. The control objective is to design an adaptive NN controller to guarantee the stability of the systems and obtain good performance. Different from the existing results, which used NN to approximate the nonlinearities directly, NNs are employed to identify the originally designed virtual control signals with unknown nonlinear items in this paper. Moreover, a sequence of virtual control signals and real controller are designed. The adaptive backstepping control method and Lyapunov stability theory are used to prove the proposed controller can ensure all the signals in the systems are semiglobally uniformly ultimately bounded, and the output of the systems can track the reference signal closely. Finally, the proposed adaptive control strategy is applied to the Puma 560 robot manipulator to demonstrate its effectiveness.
Subfields: Computational Theory and Mathematics, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/65a86bb05b11bc80ad78bb5eb36dfc7f946e0012
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In Computational Theory and Mathematics
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Citations in the corpus
- Neural-Network-Based Event-Triggered Adaptive Control of Nonaffine Nonlinear Multiagent Systems With Dynamic Uncertainties (2020, background): “According to [33], the dead-zone model is designed as follows : where m i , l and m i , r denote the right and the left slope of the dead zone, respectively, and the parameters ρ i , l and − ρ i , r are the breakpoints of input nonlinearity.”