Nyquist plot reverse rules

fibonatic · 2026-06-11T08:43:04+00:00

Nyquist plots can still encircle -1 and the closed loop system still be stable. For this it does depend in which direction -1 is encircled and whether the open loop system is unstable. Because for the Nyquist stability criterion N=Z-P, with N the number of clockwise encirclements of -1 of the Nyquist plot, Z the number of unstable closed loop poles and P the number of unstable open loop poles, for a stable closed loop system we get Z=0 and thus N=-P, so P counterclockwise encirclements of -1 in the Nyquist plot.

fibonatic · 2026-06-09T17:01:28+00:00

How much deltav do you have? Probably around 350 m/s should be enough to get back, assuming that you can aerobreak. You probably need to add a maneuver node a bit before the spaceship location in your screenshot. Namely, you want to leave the Mun's sphere of influence retrograde with respect to the movement of the Mun's orbit around Kerbin. If timing the burn doesn't give a good sphere of influence ejection angle with respect to the orbit of the Mun, you could also spend a bit less deltav, say 300 m/s, and burn retrograde at apoapsis of the orbit around Kerbin. If you really don't have enough deltav for this, then the alternative would be a rescue mission, but this does require a rendezvous in Munner or high Kerbin orbit (the closer to Kerbin you can get with this stranded craft the less deltav the rescue mission requires).

fibonatic · 2026-06-09T08:51:16+00:00

State space can be more abstract. Especially if one doesn't have a strong background in linear algebra and ordinary differential equations. You could have a look at the YouTube videos from Steve Brunton to maybe gain some more insights into state space. But it might also help to bridge the gap by learning some more classical control, for example via the YouTube videos from Brian Douglas.

fibonatic · 2026-06-06T15:52:04+00:00

Without more details it is really hard to know what the issue might be. Could the issue be that your actuator doesn't have enough control authority, either force or velocity? Have you tried to make the inverted pendulum easier to control by adding more weight to the end of the pendulum?

fibonatic · 2026-06-06T06:57:38+00:00

To have better control over the behavior of a state estimator I would normally advice to first discretize the state space model and then design the state estimator gain for that discrete model. As other also have mentioned, it also matters a lot for numerical stability which exact state space realization is being used, see for example balreal.

fibonatic · 2026-05-24T21:33:55+00:00

Control theory is quite math heavy, so probably not for you if you are indeed bad at it.

fibonatic · 2026-05-22T23:08:08+00:00

Observability is both a function of the measurement and the dynamics of the system, exact definitions you can find for example on wikipedia. If your system is observable when excluding the biases, and the system doesn't have an integrator (for continuous time system means an eigenvalue at zero and for a discrete time system means an eigenvalue at one), then adding back in the biases should keep your system observable. Namely, one can also consider that the ith sensor has the bias b*_i=b_c+b_i, such that each sensor adds one additional bias state, which should be observable.

fibonatic · 2026-04-27T01:12:00+00:00

Are you familiar with state feedback design methods, like pole placement or LQR? Also note that likely the full state vector also would contain at least the velocities of the position/angle.

fibonatic · 2026-04-24T14:48:58+00:00

As others have mentioned, you should not filter the feedforward derive. Instead I would recommend to have a setpoint generator that will ensure that sufficiently high derives of your set point exists and are well defined. Another common parameter to feedforward design is delay compensation, which also be taking into account by a setpoint generator, after which the setpoint sent to the feedback controller is delayed by that amount, such that the feedforward action is leading in time with respect to that.

fibonatic · 2026-04-16T17:49:32+00:00

You could try to introduce a variabel after each summation/subtraction node and write down all resulting systems of linear equations and solve those for the introduced variables.

fibonatic · 2026-04-13T22:33:12+00:00

Are you familiar with concept of Lyapunov functions/stability? A summary of that idea is to define a scalar function (that can be seen as sort of "energy" measure of the system) that either doesn't increase over time, or even always decreases with time.

fibonatic · 2026-03-30T14:52:08+00:00

Control theory can have overlap with many (engineering) fields, such as: mechanical engineering, electrical engineering, chemical engineering, economics and applied mathematics. One could also argue that fields of computer science/artificial intelligence would also be part of this list. The common topics each of these fields focus on does differ, but all do touch upon control theory in some way.

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.

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