For all the measurement technology we have available there are some elements of motion control that are generally missing. We have laser interferometer measuring tools that are accurate to a fraction of 1 micron. There are rotary position sensors that can divide a circle into a million digital positions. Many of the semiconductor industry’s processes would not be possible without the incredible advances of measurement technology.
Sometimes the motion control aspect of a process is not the primary objective of the control system or machine being considered. The process of clamping or crimping a can lid onto a can body is an example of this situation. The motion control system must locate the can lid to the can body correctly, but the final process is the crimping or application of a thrust force to cause the parts to form a strong joint. In this case the real process variable is the pressure that is exerted at the end of the motion. The pressure is critical to joining the parts, especially when the can is an igniter for an automotive air bag.
Grinding and polisihing is another example. The motion control application is required to bring the grinder or polisher into contact with the work piece. The actual grinding or polishing is the amount of friction generated between the grinder motor and part being worked. This is actually proportional to the current of the grinding motor, which can be measured and regulated. If too much current is detected the part might be ruined and the control system can be commanded to move the grinder away from the workpiece.
Important physical attributes of motion include inertia, center of mass and momentum. There are no convenient sensing technologies that help us with these seemingly basic attributes of the mechanical system. This is probably why they are ignored in the control system.
However, if the machine was designed in a 3D solid modeling environment, then things like center of mass and inertia are directly available. A momentum profile can be created as a product of the center of mass and the duration of the motion profile. This gives us mathematical information that can be used to “inform” the control system in spite of the absence of a control signal that directly measures these properties.
With this in mind one can easily imagine a pick and place mechanism made from two linear stages mounted one on top of the other. When the two axis are making high speed coordinated moves, the reflected forces of the upper axis put loads on the lower axis. The data from the solid model becomes useful information in providing mathematical “filters” that can improve the motion in ways that are beyond the current technology of motion control.
There are ample opportunities for improvement in the control of mechanical systems. We should be looking for new strategies that the modeling and simulation environments provide.