Adaptive Neural Network Tracking Control for Robotic Manipulators With Dead Zone

• Published in: IEEE transaction on neural networks and learning systems Vol. 30; no. 12; pp. 3611 - 3620
• Main Authors: Zhou, Qi, Zhao, Shiyi, Li, Hongyi, Lu, Renquan, Wu, Chengwei
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Artificial neural networks; Backstepping; Backstepping control; Control stability; Control systems design; Controllers; dead-zone input; Manipulators; neural network (NN) control; Neural networks; Nonlinear control; Robot arms; Robot control; robotic manipulators; Tracking control
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2018.2869375

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.