Adaptive control of manipulator based on neural network
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S.I. : SPIOT 2020
Adaptive control of manipulator based on neural network Aiqin Liu1 • Honghua Zhao2 • Tao Song3 • Zhi Liu4 • Haibin Wang5 • Dianmin Sun6 Received: 28 July 2020 / Accepted: 4 November 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract With the development of economic science and technology, the development of computer vision has undergone rapid changes, and various products relying on computer vision are also more and more, such as smart home, robot technology, and so on. At present, robot technology has become a very important part of the development of human science and technology, and in the field of industrial robots, the most rapid development is the robot with robot arm adaptive motion. It is very necessary to study the adaptive motion control of the manipulator based on machine learning. The robot with the adaptive motion of the manipulator can carry out logistics express sorting, operate in the doors and windows outside the building, and pick fruits in the orchard, which can ensure the effective implementation of hard work. Therefore, this paper proposes a mechanical adaptive control method based on a neural network. According to the motion model of the manipulator, the RBF neural network model is used to judge the stability of the system according to the Lyapunov function. The related algorithms of machine learning and multi-degree of freedom manipulator are studied and improved. The RBF neural network model approximates the unknown function infinitely and then establishes the complex motion model. Aiming at the adaptive neural network of a manipulator, a network adaptive terminal control method is proposed. Firstly, a stable manipulator motion system is designed by using a neural network, and then the terminal synovial controller is designed by using backstepping control technology. The stability of the method is proved by using the approximation virtual control technology of the neural network. The adaptive control is realized by using the learning and self-adaptability of the neural network; thus, the stability analysis of the closed-loop system is realized. Keywords Machine learning RBF neural network model Mechanical arm Adaptive control
3
School of Artificial Intelligence, Tianjin University of Technology, Tianjin 300387, China
Aiqin Liu [email protected]
4
School of Information Science and Engineering, Shandong University, Qingdao 266237, Shandong, China
Honghua Zhao [email protected]
5
Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan 250000, Shandong, China
Tao Song [email protected]
6
Department of Thoracic Surgery, Shandong Cancer Hospital Affiliated to Shandong First Medical University and Shandong Academy of Medical Sciences, Shandong University, Jinan 250117, Shandong, China
& Dianmin Sun [email protected]
Zhi Liu [email protected] Haibin Wang [email protected] 1
School of Information Science and Engineering, Qilu Normal University, Jinan 250013, Sh
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