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<p>In the digitalization of big data information, how to use big data technology to design and study the mechanical robot control system of industrial PC has become a key topic of concern in today’s society. In this paper, robot kinematic modeling is mainly used to analyze and study the robot motion equations and control system operation mechanism, and the motion controller of the industrial robot control system is selected from the CLIPPERPMAC controller of the PMAC motion controller series. The approach to reducing the operation error of the industrial robot system from the control perspective is discussed. The error is reduced by adjusting the PID parameters of the PMAC motion controller in automatic and manual ways, and the error is further reduced by introducing fuzzy control algorithms. By comparing the original curve, the curve after the PID adjustment of PMAC itself and the curve after the introduction of the fuzzy controller, the curve error is kept below 5%, which is determined to be an effective method to reduce the error. This study can precisely realize the design and research of mechanical robot control systems, thus providing a guiding reference value for the research of industrial robot mechanization. </p>
</abstract>

In the digitalization of big data information, how to use big data technology to design and study the mechanical robot control system of industrial PC has become a key topic of concern in today’s society. In this paper, robot kinematic modeling is mainly used to analyze and study the robot motion equations and control system operation mechanism, and the motion controller of the industrial robot control system is selected from the CLIPPERPMAC controller of the PMAC motion controller series. The approach to reducing the operation error of the industrial robot system from the control perspective is discussed. The error is reduced by adjusting the PID parameters of the PMAC motion controller in automatic and manual ways, and the error is further reduced by introducing fuzzy control algorithms. By comparing the original curve, the curve after the PID adjustment of PMAC itself and the curve after the introduction of the fuzzy controller, the curve error is kept below 5%, which is determined to be an effective method to reduce the error. This study can precisely realize the design and research of mechanical robot control systems, thus providing a guiding reference value for the research of industrial robot mechanization.

Research on the design of mechanical robot control system for industrial PC in the context of big data 

School of Electrical Engineering, Nanjing Vocational University of Industry Technology

Applied Mathematics and Nonlinear Sciences

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