PID control has been with us for a long time. Proportional, Integral and Derivative relationships are applied to the control of a variable whose setpoint is to be controlled. Complex relationships can be created, and they have been, that create powerful control solutions that are at the heart of many major industries.
At the heart of the PID control method is the application of math to control. The mathematical relationships are intended to be analogs for properties in the real world. And through the ability to describe real world behaviors as control variables, it makes possible the very idea of control. Because from a semantic point of view, the control voltages and software code that we use to represent the world, are not the real world events themselves. And that is the crucial element to remember.
In the realm of motor control there is a lot of stuff going on between the motor, the load and power electronics being used to control the system. There are usually multiple control control loops that are actively running in order to regulate the motor speed and current. These loops are inner loops that are indirectly controlling the load. We infer what the operating conditions are based on errors detected in the in inner regulation loops.
All variable frequency drives, servos and brush drives operate on the same principles. And that’s fine as a control strategy. But in terms of how the control system represents the real world, its very limited. Most motor systems operate without feedback on the load, so we are left with only the voltage and current waveforms to use for reference. And in spite of this limited representational model, things generally work pretty well.
Then we get into the motion control realm with applications requiring precision position and tightly coordinated control of multi-axis motion. In the motion control scenario everything is controlled by the velocity command, also called the motion trajectory. The common control mode is to use a zero to ten volt signal, sometimes -10 to +10, as the representation of motor speed. Given a fairly high resolution voltage, typically 10 bit or higher, and decent frequency response in the controller, it is possible to achieve amazing results. Using simple analog tools comparable those described above, we have been able to make CNCs run airplane parts with great precision.
The velocity command, also known as the time-displacement curve, or trajectory, represents the total work done by the axis of motion if considered as the integral over time. But the trajectory has very little ability to represent the physical load. So we have built the entire motion control industry around a system for which we have very little information. And yet accomplished great things.
It seems impossible that with all the complexity presented by coordinated motion, that the dominant control technology is still PID. PID control is an excellent technique for managing the inner loop behavior of current and voltage in the motor and control system. But it seems terribly ineffective in terms of managing the load. And as the load is purely mechanical in nature, other tools would seem to be more appropriate.
Given the huge innovations of 3D solid modeling, and the ability to integrate information across the digital domain, it would seem that some great leaps forward are coming.