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A common mistake in home built controllers is for people to think they're applying P when they're actually not. Motor controllers often have a position loop, running over a velocity loop running over a torque loop. A cascade. The first point I'd like to make is that if you're building your own PID controller you should also build a way of measuring the open loop response.
Do a frequency sweep at the input to your controller and measure the sensor's output with the feedback disconnected. Then you can draw the open loop Bode plot and see why your system is stable and be able to trade off the various controls. It's also useful to measure the closed loop response and you can do that with any system by doing a frequency sweep of your set-point while the loop is closed.
Both these aren't that hard and don't require a lot of theoretical knowledge. If you're simply tweaking controls without any understanding of what's going on under the hood you won't be able to optimize your system. Building some intuition about these systems isn't that hard. So what you're doing when you're increasing the proportional gain in all those manual tuning methods is finding the frequency where the phase goes to See this to get some more idea about the impact of the various controls on your frequency response.
Quite often getting best closed loop performance involves tweaking the system and not just the controller gains. What you want is to make the system as "stiff" as possible.
That will let you ramp up the control parameters and get the best open and closed loop bandwidth. In my experience in motor control applications the proportional gain is the one that should be doing most of the "work" and the integrator the "rest". I don't think you need a D term at all. Having a low pass filter and a notch filter helps a lot in situations where you may have some mechanical resonance but setting them without a Bode Plot is very difficult the oscillation frequency you observe under closed loop may be different than the open loop one.
If safety is a concern very powerful motors or a system that could be destroyed by the motor going out of control you need to put in some limits before you start tuning e. Then you need to get some sort of feel for the range of the parameters. If your feedback has 40 counts per rotation or counts per rotation your parameters will be a factor of for a given system. My approach would be to first find a range where you have some poor controllability and then ramp up from there starting with P and then I though again you're flying blind.
Backing off creates this stability margin.
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Closed loop attempts to take out the error from the system. It's always going to have a somewhat limited performance. What you want to do is minimize the error your closed loop controller sees and one way to do that is through a technique called feed forward. In feed-forward you go around the controller and drive a command directly to the system. An example of that would be acceleration feed-forward. If you know you're motor's torque constant and you know the load you can pretty much tell how much current you need to drive to get a certain acceleration of the load.
You simply take the command input acceleration, multiply it by a constant and add that to the controller's drive command. You're basically doing what it would take to drive the system if there was no controller and the closer you can get the less error your loop has to take out and the better your system will perform. It makes a huge difference in practice. Ziegler-Nichols is an easy manual method. More robust methods also exist - these usually rely on mathematical solutions analytic, iterative optimization, etc.
Beyond that, google "self-tuning PID" for some automated techniques.
Simple PID Tuning Diagnostic Tips
My favorite is the application of neural networks to PID tuning. There is a quicker approach called Ziegler—Nichols :. And in this image demonstrate PID parameters effects :. Thank you for your interest in this question. Because it has attracted low-quality or spam answers that had to be removed, posting an answer now requires 10 reputation on this site the association bonus does not count. Would you like to answer one of these unanswered questions instead?
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The key results of quasi-optimal PID tuning researches are given in [ 5 T. Control Eng. Process Contr. The attempts of synthesis is relatively simple PID tunung analytical methods undertaken in [ 17 P. Cominos, and N. Maslennikov, V. Meshcheryakov, and E. Dovgopolaya, "Methods of analysis of automatic control systems obeying a mathematical model with cubic characteristic equation", Autom. Aperiodic transient even with the resonant amplitude-frequency response elements in closed feedback loop by using PID controller showed in [ 24 V.
The obtained results are based on [ 25 V. The achievement of relatively simple conditions for ensuring aperiodic transients in automatic control systems with a PID controller will be extremely useful for many applications. So it is the main goal of the research presented in this paper. This inequality leads to quadratic equation analysis by dividing its components by a 2 d 2 :. Perhaps it seems that the term 3 analysis leads to the quadratic equation solving is easy.
However, in real control systems case coefficients a , b , c and d are depended by the multiple parameters of the remote control loop links. Thus, this analysis may be full of traps and pitfalls. The analysis was carried out in terms of the scanning probe microscope automatic gain control loop. We will use the PID controller with the transfer function [ 24 V. Vibration of the Z-stage is described by the quadratic polynomial.
G 0 is the loop gain. So the resulting closed-loop characteristic polynomial could be written as:. The term is legitimate because the control process resonant frequencies are over Hz and the Q-factor is about. Comparison of equation 5 and term 2 is , a where. The resulting PID tuning rule is. This term is completely in line with the results given by approximate formula for Q-factor which had been used at the scanning probe microscope remote control system design implementation [ 28 V.
Meshtcheryakov, and E. In fact, the oscillation transition and complex-conjugate poles may occur even in the context of none of loops elements with the resonant behavior. The term of oscillation inception following from the Q-factors approximate formula were obtained in [ 24 V. CASE 1. There are two elements with the real poles at their transfer functions for the automatic control gain loop in first case. The case of the real poles are equal [ 29 V. Maslennikov, and A. Substitution of to the condition 6 leads to. If , then the right hand side of the inequality 6 appears to be the negative component and the term 6 under condition 7 will be realized more.
CASE 2. There is the element with the complex-conjugate poles at the transfer function for the automatic control gain loop in the second case. In that case the terms 1 can be represented as. There are the closed feedback loop transient at the Figs. There is the blue line at the Figs. There is the red line at the Figs. Obviously, the I regulator usage leads to increasing the high frequency noise for the case 2. Also, the oscillations in transient have not been damped without PID regulator, only moved to the initial section.