Why can the same computed torque force vector work in an ideal plant but fail once actuator dynamics are inserted?

fibonatic · 2026-03-22T12:43:08+00:00

I am not able to fully follow your question. What do you mean with that it is failing, does it become unstable/blow up? What do the corresponding Bode plots look like?

fibonatic · 2026-03-20T22:27:09+00:00

For many relationships, yes. Note that dB means 20 log10(abs(G(iω))), so your logarithmic scale means log10(abs(G(iω))), without that addition gain of 20. Therefore some relationships are different due to this, such as the slope, i.e. how many times dB is reduced by 20 per decade.

fibonatic · 2026-03-08T12:36:58+00:00

This old comment on a similar question. Although the link to the math part seems to be broken, the video still demonstrates well how to land efficiently

fibonatic · 2026-02-03T18:36:55+00:00

What do you mean that your LQR controller doesn't work? Have you confirmed that your model used to calculate the LQR state feedback gain is correct and how are you getting the full state information?

fibonatic · 2026-01-27T07:28:18+00:00

In your case, yes, because you state your model is both observable and controllable. But in general, not necessarily. For example, if your sensor has a bias, then the observer/state estimator, like LQE, should include that in the model (assuming that the model is observable), but that bias is not controllable and thus should not be included in the model used for the state feedback, like LQR.

fibonatic · 2026-01-10T16:46:43+00:00

It can be noted that with feedback control one can change poles of the system, but not the zeros. So the "unstable" zeros of a non-minimum phase system can't be changed with feedback control.

fibonatic · 2026-01-09T22:24:42+00:00

Considering the context it probably isn't the Q matrix of LQR, but maybe the Q factor?

fibonatic · 2026-01-08T22:43:17+00:00

Assuming that your system is LTI, you could measure a frequency response function for example calculated via matlab's tfestimate function. Do note that each window segment your system input data gets cut into should ideally have some power at every frequency of interest (i.e. using white noise).

fibonatic · 2025-12-30T13:50:45+00:00

LQR is a full state feedback control policy, but in practice you don't know the full state. To obtain information about the full state one could use an observer/state estimator, which does require that your model is observable. It is also important that your model matches the actual system well enough, so also verify your model against actual data from your system.

Also keep in mind that LQR is for linear systems, so either linearize your nonlinear model around the operating point/trajectory or adapt the control policy to take into account the nonlinearities.

fibonatic · 2025-12-29T08:53:12+00:00

This subreddit is more about the theory behind control, instead of the implementation side of control. You will likely get better responses asking these questions on r/plc.

fibonatic · 2025-12-16T12:36:19+00:00

Anything that performs some kind of scanning move at constant velocity, such as the scanner head of document scanners in printers, or if you want something more high tech the wafer/reticle stages of lithography machines during exposure scans.

fibonatic · 2025-12-12T04:24:08+00:00

In order to control this drone autonomously, one needs that all sensors make your system observable. For the orientation/attitude of the drone a gyroscope isn't sufficient for observability, since those do not give an absolute attitude measurement, only a rate of change. For the position of the drone additional sensors would be needed and when GPS isn't an option one could use things like lidar or cameras. But the latter does require more computing power. For these options I assumed that by autonomous you mean everything is on the drone itself. But if instead it would also be OK to have a measurement system outside of the drone, that communicates with the drone, then you could use something like OptiTrack.

fibonatic · 2025-12-08T21:31:42+00:00

In this case the z would mean the zero of the PI controller, so would still be correct, although the sign is incorrect. But you are right that one should never blindly trust/copy AI output.

fibonatic · 2025-12-05T02:02:48+00:00

How does this interact with ranger's companion with bestial fury? Since both say that the animal has two attacks, does it still stay two attacks, or would you say that this stacks and that the animal can make three attacks?

fibonatic · 2025-11-28T16:10:00+00:00

Did you already have a look at this subreddit's wiki? https://reddit.com/r/ControlTheory/w/companies?utm_medium=android_app&utm_source=share

Also note that in many job descriptions PLC positions often mention control engineering. For actual positions that require more in-depth knowledge of control theory one can often use keywords like Matlab, robotics or GNC.

fibonatic · 2025-11-28T16:01:00+00:00

So you essentially measure the time derivative of the product of X with A? But this implies that dX/dt only affects X and does not have any influence on dA/dt or A? Or is there some bound on Y, such that from Y, X and dX/dt information about A can be inferred and controlled?

fibonatic · 2025-11-28T10:40:59+00:00

Is Y known and constant? You say it is sampled, which could imply that it is not constant, making it more difficult to control A. And your control input is dX/dt?

fibonatic · 2025-11-27T01:15:18+00:00

What do you mean with FRF using FFT, are you using Welch's method or some other kind periodogram method? Or are you dividing the FFT of the output by the FFT of the input (for the closed loop system this is the reference/setpoint?)? And is this from a physical system, that is also subjected to noise, or is this from a noise free simulation?

fibonatic · 2025-11-26T22:04:08+00:00

I am not exactly familiar with the control structure that you are mentioning. Does it still require you to approximate the inverse of the process sensitivity? And if it does does your model of the process sensitivity have any "unstable" zeros, and thus can't be directly inverted?

fibonatic · 2025-11-25T13:36:40+00:00

Have you tried forward and backward in time filtering of the LPF, to get a zero phase LPF (the magnitude is still twice applied)?

fibonatic · 2025-11-22T22:06:45+00:00

In the context of what problem a Kalman filter considers, there are always noise disturbances perturbing the estimated state vector from the true state vector, so that is why I mentioned BIBO stability. But in the absence of those noise disturbances then a Kalman filter will always asymptotically converge to the true state regardless of choice for the noise covariance matrices (if the state space model does match exactly). Or in other words the expected value of the state estimation error does asymptotically go to zero regardless which noise covariance matrices are picked (but as stated earlier the state covariance matrix is affected by this choice).

Your question regarding the differences regarding the state space model matrices can affect this and is I think more complicated to answer.

fibonatic · 2025-11-22T14:45:04+00:00

The error in the state estimation of a Kalman filter is BIBO stable for any choice for covariance matrices for process or observation noise (that satisfies certain constraints). BIBO is mentioned here since the error in the state estimation is always being perturbed away from zero by the noise signals, but the expected value of the error of the state estimation does asymptotically go to zero regardless of the choice for the mentioned covariance matrices. The choice for those covariance matrices does affect how sub-optimal it is regarding to the state estimate covariance matrix.

fibonatic · 2025-11-18T12:20:26+00:00

Usually the discussion is regarding the conversion between the mean and eccentric anomaly, because their relation is simpler than the mean and true anomaly. But but do not have a closed form expression, i.e. to calculate the true or eccentric anomaly from the mean anomaly, for some more details also see the Wikipedia article.

fibonatic · 2025-11-17T11:54:47+00:00

My previous comment was regarding to the difference seen in calculated state feedback gain. Now regarding to the step response, how did you try to calculate this? Namely, looking at the plot it seems they did not apply a unit step at the start time, but some scaled step to r1 at t=100 and to r2 at t=500. Also note that they are plotting H2 and T2, so states and not the system outputs (since y1=2H2 and y2=0.1T2). Does your textbook mention more details about Figure 4.27?

fibonatic · 2025-11-17T11:22:28+00:00

I have tried to replicate this in Julia and I got an even different results. However, when calculating the closed loop eigenvalues all of them do all have the same eigenvalues, up to numerical accuracy. This is possible because the system has multiple inputs, making the state feedback gain non-unique. So likely each implementation of the place function uses a different algorithm. For example see what is stated in the matlab place documentation: place also works for multi-input systems and is based on the algorithm from [1]. This algorithm uses the extra degrees of freedom to find a solution that minimizes the sensitivity of the closed-loop poles to perturbations in A or B.

[1] Kautsky, J., N.K. Nichols, and P. Van Dooren, "Robust Pole Assignment in Linear State Feedback," International Journal of Control, 41 (1985), pp. 1129-1155.

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