# bode plots

• posted

this is a really stupid question.

I am finishing 4 years of electrical engineering so I'm familiar with Bode plots. we've seen bode plots in many courses, including control systems, however, never once in my student life has nayone told me how they're actually supposed to be used or what they tell me about a system.

I can look at a magnitude plot and I know that a peak means there's a pole near that frequency, but how does this plot help me in stabilising a control system?

what does the phase plot tell me? what's the difference between two systems with identical magnitude plots but different phase plots? I know phase difference means time delay, so does this mean a control system with a phase lag at a certain frequency will respond slower than another system with less phase lag?

looking through Matlab documentation and case studies, I often see such bode plots and they are supposed to tell me the system is good, but I have no idea why...

• posted

Control systems often depend on negative feedback. Time delay or other kinds of phase shift that increases with frequency will turn negative feedback into positive feedback, once the phase shift reaches

180 degrees, compared ot the low frequency phase. If the loop gain has not fallen below 1 by the frequency that has that additional 180 degrees of phase shift, the resultant positive feedback will reproduce or increase an infinite series of echoes (oscillations). Bode plots of loop gain and phase shift help you determine if the loop has a gain that has fallen below 1 before the phase shifts 180 degrees.

See:

• posted

Note that you can also use Bode plots with discrete-time systems: instead of scanning s around the stability boundary (by calling it omega and adding a j, etc., etc.) scan z around the stability boundary by setting it equal to exp(j theta) and incrementing theta through a half-circle.

This tells you everything about a discrete-time system that a "j omega" bode plot does for a continuous-time system. The only real difference is that with a continuous-time system you can construct a bode plot by hand, with discrete-time you're better off just plotting it with a computer.

• posted

Boy, this would have been a good question to ask one of your instructors in school, during class. What do you think you were paying them for?

You have to distinguish the type of system that you are looking at. If you are looking at a closed-loop system the plot typically gives you performance information, for example if you are looking at closed loop Bode of a servo control system. Bandwidth, steady state error, if any, high frequency roll-off.

Open-loop Bode plot can be used for stability information, phase/gain margin, and can tell you where compensation is needed.

Because there is a one-to-one relationship between the amplitude and phase response, you won't have identical magnitude plots but different phase plots, unless considering pure phase additions, like the Pade approximation for a delay.

You have to distinguish between steady state frequency response, and transient time response. In the steady state frequency response, the lag appears as a delay, or shift of the sine plot relative to the sine plot of the input. For transient time response, it depends where the lag appears in the Bode plot and what is causing it.

To gain an understanding you'll have to go through case studies. Find a good set of lecture notes or publications that are applied.

• posted

Things you get from a bode plot are

Performance--the bandwidth indicates how fast the system can move.

Accuracy--the low frequency gain indicates settling accuracy.

Stability--the margins (gain and phase) are a measure of stability.

Compensation--you can easily apply networks which alter the margins.

dave y.

• posted

No. It means you have an integrator in the loop. For a sinusoidal input it means the output sine will lag behind. If you have a derivative in the loop, a sinusoidal input will create an output that leads the input. This does NOT mean you are predicting the future.

When a sine voltage is applied to an inductor, the current lags. When a sine current is applied to an inductor the voltage leads. Does this mean that the voltage is somehow anticipating the current just like my dog runs ahead of me? Nope. the dog only leads me when I am going in a straight line. When I change direction she has to recover and change and then speed up to get ahead of me again. In the same way, the voltage only leads during steady state when terms like frequency and phase have meaning. If you suddenly apply a sine input, the voltage will appear as a spike the instant you apply the current. It will NOT 'lead' the current until steady state occurs. Similarly, when the current is abruptly stopped, the voltage will show a strong negative spike and then decay. It will not 'lead' the current during the transition. It is not a predictor.

• posted

Fred.

• posted

Actually it is useful. But only if you also know the down & dirty classical stuff. I've found that in a DSP chip a state-space implementation takes advantage of the DSP's MAC instruction and executes very fast, so that's how I do it. But the actual _design_ for a SISO system is all classical block diagrams & bode plots.

You're being harsh. Yes, using an adaptive, fuzzy or neural controller where a plain ol' PID is optimal is just stupid, and so is using noise reduction techniques in quiet environments, but such techniques _do_ have their places. On of my favorite control texts is Astrom's "Adaptive Control", in part because he has one whole chapter examining when adaptive control _isn't_ appropriate. Every advanced-technique book should have such a chapter, in my opinion.

If you're complaining about kids out of grad school wanting to use the most sophisticated methods on the simplest problems then you're just railing against the natural tendency of Youth. Do you mean that when _you_ were fresh out of school _you_ weren't proposing op-amps and FET drivers when a limit switch and a relay wouldn't do?

Actually much of the current research literature _is_ about nonlinear control, and you can publish voluminously and quickly. It's just that the math is involved, difficult to understand, computationally intense, and somewhat divorced from real-world effects. So it doesn't bubble down to the real practitioner very well.

That doesn't mean that there shouldn't be more practical instruction in control. Control theory is all well and good, the math is absolutely beautiful (at least that's what I think of the parts that I can understand), but real control system design has more to do with understanding the behavior and limitations of the plants and controllers than it does about highfalutin math.

• posted

I agree with what you say, but I think the point I was trying to make was missed, perhaps due to poor exposition on my part. Fuzzy/neural/adaptive control or whatever is fine and the work of people like Karl Astrom, Graham Goodwin etc is excellent, but many new grads are going to these advanced methods for absolutely the wrong reasons because they have been so poorly instructed in the basics. My esperience has been that once I sit down with the new grad and explain how classical control theory is supposed to work, then the "wow" factor kicks in and they get enthusiasm to follow what I think is the right path, namely start with classical and then move onto the exotic stuff later on when and if needed.

About the latest research - the problem is that the researchers are usually mathematical types with no practical control engineering experience and as such they don't really understand the engineering process nor its problems. A lot of control and signal processing research is being hijacked by mathematicians that can't compete in the math journals, so they look for other topics to publish in. Most of the time their research, while very interesting doesn't really help the engineer battling with nasty servo nonliearities etc.

Perhaps my harshness should rather be interpreted as a "cry for help" on behalf of engineers working on difficult problems. We need a Henri Poincare type of person who has a handle on nonlinear mathematics and physical reality to come to our aid.

Fred.

• posted

Hear hear. The best nonlinear control theory I have comes out of books written in the '50s. Since it's designed to be graphical it's easy to understand, and since it's from before computers they don't even try to use the super-zoot math -- they just show you how to make it work from physical measurements.

Part of the struggle with servo systems is that you can't make a silk purse out of a sow's ear. Bode proved a theorem (in the '50s) that you couldn't improve the overall sensitivity of a system to disturbance, you could only push the sensitivity around. Since we usually want to push the sensitivity _up_, and since everything conspires to limit the control system bandwidth, this puts an upper limit on what you can do in the real world with any given system.

• posted

Didn't Bode publish his sensitivity theorem in his 1940 paper? I know his book of 1945 contained it.

I agree, the most useful practical stuff comes out of the ~40's servo and circuit theory (pushed by the war effort) by many great engineers and mathemageers (like Bode and Nyquist) working at MIT labs and "the telephone company" etc. Good 'ol describing functions and the like. I think part of the problem is the communication gap between academia and industry that now exists in publications due to the "theorem-proof" style of paper that was introduced by the hijacking mathematicians. Practicing engineers often get good papers rejected because the journals are controlled by these "theorem-proof" guys (I have heard some fascinating anecdotes about this!).

The important thing about control is the concept of bandwidth and, as pointed out by Horowitz in 1959 (the first QFT paper and the introduction of 2DOF systems) it is the "cost of feedback" in terms of noise beyond crossover. He showed that arbitrarily large parameter variations can be handled by LTI compensators once the price in terms of bandwidth and its associated requirements on modelling and noise are understood. He also threw the cat among the pigeons with his challenge to the adaptive guys to prove that LTI controllers could not do what adaptive ones were supposed to do - this sparked a great debate and lots of useful research leading to the proper understanding of "robustness" - a concept that Bode figured out years previously.

Fred.

• posted

Thanks, Fred

...for reminding me of the root of the reason for my present dedication to classical methods for teaching undergrads! Your treatise could have been an excerpt of a Control II lecture given to me in 1992 (perhaps by you - to reply, knock out the "o")

In full support of the views expressed in the foregoing dialogue - my firm belief remains, that no matter how advanced the theory and analysis becomes, "plant is plant" and remains eternally so, as do classical methods remain eternally valid.

My favourite final approach for a first course in control is the use of the Nyquist criterion and Bode plots to find the simplest form of control of all kinds of (possibly unstable) system, for various performance/cost objectives. By all accounts, the (very old) classical approach has worked - judging by the "ah ha's" detected, and the resurgent interest in control-orientated 4th year projects in our School.

cheers,

John

• posted

............ >

This has been an interesting discussion. Being self experiensed the transsion from control theory researcher to control engineering practitioner, I fully understand the strong feeling of Fred, still more appreciate the viewpoint of Tim. It seems to me that "controls", when solving real world problems, involves a lot more of the fields it is been used. This sounds obvious, but is quite deep. For example, I know a lot of people in this newsgroup come from the field of process control, where PID is dominant and a major control work seems to be tuning the gains. But in automotive, a big part of the control work is to determine the status of the system dynamics, where observer and estimation techniques have been instrumental. I can image in some aerospace control problems, state-space methods are more feasible to deal with the multi-IO stuff. Also, as control algorithms are mostly implemented as embedded software, computational aspects become more and more important. For problems with large dimensions, it has been proven that state-space format is more efficient and better conditioned than transfer functions. All I try to say is that "control" is so multi- faced, it involves domain knowledge (plant), software, hardware(sensors, actuators, processors, electronics), communication (CAN etc), and a lot of maths (linear algebra, numerical analysis, etc). It is both a bless and a curse in the sense that you can work on (learn) a lot of stuff, but have to know a lot of stuff to do your job right. Finally, I sense the control community has been trying to "rescue" control from the "hijacking by mathematicians" by establishing application oriented journals like IFAC's Control Engineering Practice and IEEE's Transactions on Control Systems Technology. Just hope the "gap" between theory and application of the wonderful field of controls becomes smaller and smaller - by working toward the same goal from both sides!

RT

• posted

When was getting IEEE's "Transactions on Control Systems Technology" I felt that it merited "hijacked by mathematician" status and discontinued it (2-3 years ago). I still subscribe to "Transactions on Automatic Control", but I probably get through one article a year.

How is the IFAC journal?

• posted

The point I am trying to argue pertains to the cognitive science aspect of design. When doing a home improvement project, one uses screwdrivers for screws and hammers for nails. It is most efficient to use the tools most appropriate for the job. This is where I have a problem with the overemphasis of state space methods in design. I have no problem with state space ideas themselves, only the way they are misapplied in the design context.

State space models are descriptions of dynamical systems where the detailed inner structure of the system is exposed. They are very useful for simulation purposes and have important numerical advantages over transfer functions in analysis - for example in the computation of balanced realizations etc.

It can be argued that having a more detailed model is a good thing and indeed, this is true if you are a physicist, in which case a quantum mechanical model is even better than a state space one.

While a state space model is good for ANALYZING a complex dynamical system in order to understand its behaviour, it is normally too detailed for the purposes of DESIGN. This is because engineering design is all about tradeoffs and can be regarded as the artful application of the laws of physics to accomplish a desired goal. The laws of physics are the engineer's paintbrush.

However, for an engineer, there is the important idea of model fidelity - use the model which has just the right amount of detail to give you the answer you are looking for. This is the "economy of representation" concept. Because tradeoffs are so important in design, a good design technique requires good indicators of the design quality.

Humans have incredibly powerful pattern recognition capabilities and are able to rapidly assess tradeoffs given good visual indicators (for example, that's why analog instruments are used in aircraft cockpits rather than digital ones - pilots can quickly scan them, especially in emergencies). In control system design, a good visual indicator of performance is a frequency response plot as it summarizes all the salient features of (good or bad) performance.

I think that control system design can be accurately described by saying that the performance of a feedback loop depends entirely on the "shape" in magnitude and phase of the loop transmission function L(s). I believe that this statement is the heart of control system design. Therefore, designing a good control system requires changing the shape (the energy-frequency profile) of L(s) - a process known as "loop shaping". The frequency domain lends itself particularly well to this strategy, because it allows "point-by-point" design at each frequency via satisfaction of a simple phasor equation: 1 + L(s) = 0 without requiring extensive matrix manipulation and subsequent detailed interpretation.

An important aspect of this technique is that it provides a highly transparent indicator of the important tradeoff between complexity and performance. The number of poles and zeros required to approximate a desired loop shape is clearly visible in the frequency domain. (How many people can take a state space controller matrix and evaluate its performance at a glance?)

In addition, the vitally important concept of bandwidth, so important with respect to noise, cost of feedback and saturation is clearly highlighted. Such indicators are opaque or nonexistent in the state space formulation unless a lot of extra analysis and interpretation takes place.

State space methods have the following disadvantages in design:

*) They are strongly dimensionally dependent - a second order state space model and a 50th order one are vastly different in complexity. *) Convolution integrals and differential equations are the basic tools - not easy to work with for most people *) NMP zeros are not readily apparent - additional analysis and calculation is required to discover them *) Tradeoff indicators between performance and complexity are not readily evident *) Costs of feedback, noise bandwidths etc are absent or not transparent *) Too much extraneous detail - poor economy of representation

Frequency domain techniques have the following advantages:

*) Point-by-point design using algebraic phasor analysis and simple multiplication *) No strong dimensional dependence - a 1st order frequency response and a 50th order one reveal the same information at a glance *) Complexity/performance tradeoffs are highly trasparent with simple interpretation *) Good graphical indicators of T(s) and S(s) *) The right amount of detail for successful design

State space is good for:

*) Numerical simulation of complex processes *) Implementation of controllers in firmware/hardware *) Nonlinear systems analysis *) Robust mathematical computation *) Anaysis and understanding of complex behaviours

I have met many recent grads that leave college with the idea that state space is some how superior to classical transfer function methods. Also, I have seen them completely stumped with a simple problem that becomes trivial with a simple frequency domain analysis. Many of their professors have NEVER designed and implemented a controller in their entire lives!

Both techniques have their place as mentioned above. However, in the design context, state space methods obscure many important details on the one hand, while supplying too many extraneous unimportant details on the other. I would go so far as to say that state space methods combined with faulty logic (especially in the use of observers) have actually delayed the progress of research in control system design, but that is another chapter in the saga!

State space was originally conceived to handle nonlinear systems, where differential equations are naturally the main workhorse and where it is the most appropriate theory.

History has vindicated the original proponents of the frequency domain in the face of severe criticism by state space workers during the early days. Statements by eminent researchers saying that "The Laplace transform is dead and buried" have done much damage to the frequency domain cause, but the critics have been proved wrong as control system design has come full circle from the early state space LQR days in the

1960's back to the frequency domain with techniques like H_infinity today.

Perhaps the great attraction of state space is the elegant mathematics and "neat" ideas it contains. Like a siren, it draws the sailors onto the rocks...

• posted

Now I know why more people (especially young guys as Fred rightfully pointed out) like the state-space method than the transfer function format. It's a siren! :-)

Control design basically involves two parts: the plant and the controller. The better we can know either part, the better we can achieve the control objectives.

Thanks, Fred and Tim, for the long and insightful posting.

-RT

• posted

Huh? Good for Nonlinear? That's a new one on me. State space is purely a linear technique. There are some instances where is is strained for nonlinear analysis, but I've never seen any that were very useful. For nonlinear analysis, the just do a robust design at one operating point, and then schedule the results over multiple operating points.

Agreed.

Huh? Again you lost me. State space is just linear algebra using vectors and matrices to solve constant coefficient, linear partial differential equations. No nonlinearities there.....

• posted

I've found the UK publication: Control Engineering Practice to be a good combination of theory and practice. The ASME's Journal of Dynamic Systems, Measurement, and Control is another reference with a a good combination of theory and practice.

IEEE Transactions on Control Systems Technology went the way of IEEE's Automatic Controls,...no fun to read.

• posted

Hi!

I have to admit that I am puzzled at the unjust bash> >>State space is good for:

I don't get this one. How else can one implement it? The only two things missing are paper and software. The latter, I guess, is implicitly mentioned in "Numerical simulation of complex processes" above. So, what is the point?

dx/dt=f(x,u,t,v) y=h(x,u,t,w)

Here is a state space representation. x is the state vector, u is the input, t is time, v and w are disturbances or noise sources. How can you be so sure that f() and h() are linear functions?

Andrey

• posted

Whoever "they" are, they have the luxury of working on easier systems than I have. While some of the nonlinearities I have dealt with can be linearized away, friction, backlash, and actuator saturation cannot be ignored or linearized if you want to come close to an optimum trajectory from point A to point B -- and using a low-order nonlinear state-space representation as shown by the OP is often the clearest way to represent the system.

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