Synchronization of Electrical Drives via EtherCAT Fieldbus Communication Modules

Synchronization between devices (in particular drive systems) is paramount for multi-axis motion control systems used in Computerized Numerical Control (CNC) machines, robots, and specialized technology machines used in many areas of the manufacturing industry. EtherCAT is an Ethernet-based network that is one of the most popular industrial networks for multi-axis motion control systems. EtherCAT is standardized in the IEC 61158 and IEC 61784 standards. In the article, an EtherCAT communication network for electrical drives is presented. The article focuses on the synchronization in the EtherCAT network consisting of one master device and slave servo drive devices. Special attention is given to synchronization mechanisms in EtherCAT, such as distributed clocks in slave servo drives devices. For this purpose, a laboratory stand was built consisting of two prototype servo drive devices with BLDC motors equipped with EtherCAT communication modules. A description of the working developed EtherCAT communication modules is given. Authors in communication modules ware used an EtherCAT Slave Controller (ESC) chip (AX58100) to implement lower EtherCAT layers. EtherCAT application layer was implemented in software form on a 32-bit microcontroller, based on CANopen over EtherCAT (CoE) CAN in Automation 402 (CiA402) profile. This research’s main contribution was to show the time dependencies regarding synchronization in terms of data flow in the EtherCAT communication stack in slave servo drive devices. The research results showed that the synchronous operation of drives is mainly influenced not by the mechanism of distributed clocks that ensures synchronization in the EtherCAT network but the implementation of the highest layer of the communication stack in slave servo drive devices. Experimental results are presented that prove the modules’ adequacy for use in high-performance motion control systems.

On three-term conjugate gradient method for optimization problems with applications on COVID-19 model and robotic motion control

The three-term conjugate gradient (CG) algorithms are among the efficient variants of CG algorithms for solving optimization models. This is due to their simplicity and low memory requirements. On the other hand, the regression model is one of the statistical relationship models whose solution is obtained using one of the least square methods including the CG-like method. In this paper, we present a modification of a three-term conjugate gradient method for unconstrained optimization models and further establish the global convergence under inexact line search. The proposed method was extended to formulate a regression model for the novel coronavirus (COVID-19). The study considers the globally infected cases from January to October 2020 in parameterizing the model. Preliminary results have shown that the proposed method is promising and produces efficient regression model for COVID-19 pandemic. Also, the method was extended to solve a motion control problem involving a two-joint planar robot.