The economic realities of the industrial marketplace are driving machine builders and integrators to get creative and build more intelligence into their machines in order to increase productivity and lower lifecycle cost. This is done by minimizing time to communicate with the motion controller, and by setting up and optimizing the closed-loop controls that squeezes the last few unneeded time cycles from a machine operation. Therefore, by looking at how a motion controller connects, controls, and can optimize a system, the best performance per dollar invested can often be accomplished.

In order to maximize productivity out of a machine containing distributed computing and control elements, the machines need to be enabled to share information quickly and reliably. The proliferation of machines that are based around industrial fieldbuses such as EtherNet/IP and Profibus/Profinet is driving motion controller vendors to make fieldbus network interfaces standard on their products. Ethernet is a natural choice, because most humanmachine interfaces (HMIs) are PCbased today, and Ethernet is the defacto standard for PC connectivity. The Ethernet links to motion controllers can also be used to program the motion profiles, since virtually all motion controller software development environments are hosted on the PC. Some motion controller vendors also provide software drivers that enable PC-hosted control and data acquisition software such as NI’s LabVIEW to interact with the motion controller directly over the network. An added benefit to networking is the ability to solve problems more quickly. For instance, a networked system can be tuned anywhere in the world over Ethernet if the motion controller has Ethernet.

Closing The Loop

Delta Computer Systems motion controllers RMC75 (left) and RMC150 (right) exemplify multi-axis controllers with optimizing capability.
It’s common knowledge that closedloop controls lead to more productivity and less waste in industrial environments. For some applications, the simple proportional-integral-differential (PID) control loop is satisfactory. For an increasing number of applications, squeezing as much speed out of a machine as possible requires more sophistication. Today’s motion controller manufacturers are engaged in creative algorithm engineeering, embedding powerful mathematical expressions within the command structure of modern controllers. Clever control engineers can use these functions to perform complex motion profiles, decreasing hydraulic shock by smoothing accelerations and decelerations, and compensating on-the-fly for changes in the machine’s environment.

Some controllers can electronically synchronize the functions of multiple motion axes, “gearing” them together using mathematical relationships to ensure that some functions don’t become a bottleneck as the speed of motion is increased to improve productivity (or decreased for testing the machine’s operation). Predictive terms, called feed forwards, are added to the control algorithms to help drive the motion and eliminate positioning errors. In the case of motion controllers that have special capabilities for fluid power applications, the ability to control the pressure or force that is being applied by the motion system can produce a more precisely manufactured product.

The process of tuning and optimizing motion systems has also made great strides over the years. In the old days, tuning a machine for optimal performance was a time-consuming process, often adding much unplanned time to a machine’s development schedule. Because precise tuning was difficult, designers typically settled for a machine that performed sub-optimally. Since it was hard to make effective tuning changes, machine builders were often forced to make difficult compromises.

New motion controllers are saving time and money and enabling the construction of better products by delivering built-in graphics along with modeling and simulation tools that enable automated “point and click” system tuning. Because it is quicker and easier to evaluate whether a particular control strategy provides a better result than alternatives, engineers are free to experiment with new options such as second (and higher)-order equations as control models, variations of PID like I-PD, and control techniques such as active damping that allow the controller to adjust to challenging dynamics in the machine environment. In addition, the new motion simulators enable engineers to test out their machine’s functionality comfortably and safely on their desktops, before a machine’s hardware is actually assembled on the plant floor.

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