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robot algorithm based on neural network and intelligent predictive control

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年第12期11卷 6155-6166页

作者： Wang, Yini Chongqing Business Vocat Coll Dept Publishing & Media Chongqing 401331 Peoples R China

With the improvement of industrial control requirements and the development of control theory and computer technology, it is more and more urgent to study the intelligent predictive control algorithm with good control effect, strong robustness and suitable for more complicated industrial processes. This paper proposes a novel intelligent predictive control scheme that uses a neural network intelligent predictive controller to control the force/position of the robot. The controller of this neural network can arbitrarily approach the uncertain object of the industrial robot without knowing the exact structure of the system. At the same time, due to the addition of intelligent predictive control, the system is easy to calculate online and the quality of control is improved. It can be seen from the simulation results of the robot that the traditional PID can not solve the uncertain object well. With the controller designed in this paper, the robustness and rapidity of the system are improved to some extent, and good control accuracy and control effects are achieved.

关键词： Neural network Predictive control robot algorithm Robustness

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Complex industrial automation data stream mining algorithm based on random internet of robotic things

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AUTOMATIKA 2019年第5期60卷 570-579页

作者： Cui, Lianhe Qiqihar Univ Dept Engn Qiqihar Heilongjiang Peoples R China

In recent years, with the continuous development of computer application technology, network technology, data storage technology, and the large amount of investment in information technology, enterprises have accumulated a large amount of data while transforming and improving enterprise management modes and means. How to mine useful data, discover important knowledge and extract useful information has become a hot topic of current research. Industrial big data is significantly different from traditional big data. The traditional big data is based on the Internet environment. Although the data has a high degree of discretization and distribution, its association is relatively simple. The collection of industrial process data is relatively easy, but the mathematical and physical and chemical mechanism models involved make the inherent relationship of data complex, so it is difficult to use common analytical models and methods for processing. In this paper, we propose a complex industrial automation data stream Mining algorithm based on random internet of robotic things, and experimental results show that the proposed algorithm has higher data mining efficiency and robustness.

关键词： robot algorithm Stochastic programming algorithm Internet engineering complex industry automation data data mining algorithm

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