Science of Tuning Servo Motors (part 1)
The question most often asked is how to properly tune a motor. It has always been considered some form of black magic by those who have not had many opportunities to perform a tuning operation and a painfully learned technique to those who have.
Although there are many good tuning methods and self-tuning algorithms available, most of them are keyed to specific brands of motion controllers or to specific types of operations. To this point, no good generic tuning tutorial that covers all types of gain algorithms is found. Using complex math to solve problems is doable, but very practical in real life. Bode plots are interesting, but they are not designed for novice users. As a matter of fact, the Bode plot puts a lot of effort into determining what might happen in a given system only if all system parameters are known. And rarely in motion systems is everything known. One reason for this is because of the sheer number of parameters that affect the tune. Type of grease on the bearings, the type of bearings, mechanics in general, the machine, and the type of motor, the amplifier, friction, the environment, the motion controller, the computer ... and on and on. When tuning, rather than teaching the user how to dynamically tune correctly, each vendor has come up with his or her own special way of conquering tuning situations. The result is that the user watches and does, but learns very little about the user’s dilemma. What, for instance, is notch filtering, and when is it needed? What are the benefits of velocity and acceleration feed forward? Modeling of systems works great until an unforeseen condition is realized. You set up the filter conditions based on measurements taken, then you hope over time the conditions remain the same, or at least close to it. In the likely event they don't, however, retuning is required to once again stabilize the system. It is interesting just how many vendors have actually tried to tune a system in a hostile environment where the elements are stacked against the tuner. Here's a common situation. When tuning a system, there can be many different values entered into a given gain algorithm that will work, but only one that will work over the broadest possible range of load and motion variations. By achieving optimum gain values, other types of gain modifiers notch filters, adaptive tuning, feed forward, etc. - may not even be necessary. But if they are, then they can simply be used to assist the main gain structure in doing its job, rather than taking the place of the main structure. The question here is how do we achieve the optimum primary gain control values?
Although there are many good tuning methods and self-tuning algorithms available, most of them are keyed to specific brands of motion controllers or to specific types of operations. To this point, no good generic tuning tutorial that covers all types of gain algorithms is found. Using complex math to solve problems is doable, but very practical in real life. Bode plots are interesting, but they are not designed for novice users. As a matter of fact, the Bode plot puts a lot of effort into determining what might happen in a given system only if all system parameters are known. And rarely in motion systems is everything known. One reason for this is because of the sheer number of parameters that affect the tune. Type of grease on the bearings, the type of bearings, mechanics in general, the machine, and the type of motor, the amplifier, friction, the environment, the motion controller, the computer ... and on and on. When tuning, rather than teaching the user how to dynamically tune correctly, each vendor has come up with his or her own special way of conquering tuning situations. The result is that the user watches and does, but learns very little about the user’s dilemma. What, for instance, is notch filtering, and when is it needed? What are the benefits of velocity and acceleration feed forward? Modeling of systems works great until an unforeseen condition is realized. You set up the filter conditions based on measurements taken, then you hope over time the conditions remain the same, or at least close to it. In the likely event they don't, however, retuning is required to once again stabilize the system. It is interesting just how many vendors have actually tried to tune a system in a hostile environment where the elements are stacked against the tuner. Here's a common situation. When tuning a system, there can be many different values entered into a given gain algorithm that will work, but only one that will work over the broadest possible range of load and motion variations. By achieving optimum gain values, other types of gain modifiers notch filters, adaptive tuning, feed forward, etc. - may not even be necessary. But if they are, then they can simply be used to assist the main gain structure in doing its job, rather than taking the place of the main structure. The question here is how do we achieve the optimum primary gain control values?