Control Tutorials for MATLAB and Simulink (2024)

Below is the function lnyquist.m. This function is a modified version of MATLAB's nyquist command, and has the same attributes as the original, with a few improvements. The function lnyquist.m plots

(log2(1+abs(G(jw))),angle(G(jw))

in polar coordinates by taking the log of the magnitude, the magnitude scale is compressed and the overall shape of the Nyquist plot is easier to see on the screen. We use log base 2 and add one to the magnitude so as to preserve the key attributes of the -1 point for the Nyquist plot.

The lnyquist function also takes poles on the imaginary axis into account when creating the Nyquist plot, and plots around them.

Copy the following text into a file lnyquist.m. Put the file in the same directory as the MATLAB software, or in a directory which is contained in MATLAB's search path.

 function [reout,imt,w] = lnyquist(a,b,c,d,iu,w) %LNYQUIST Nyquist frequency response for continuous-time linear systems. % % This Version of the NYQUIST Command takes into account poles at the % jw-axis and loops around them when creating the frequency vector in order % to produce the appropriate Nyquist Diagram (The NYQUIST command does % not do this and therefore produces an incorrect plot when we have poles in the % jw axis). % % NOTE: This version of LNYQUIST does not account for pole-zero % cancellation. Therefore, the user must simplify the transfer function before using % this command. % % LNYQUIST(A,B,C,D,IU) produces a Nyquist plot from the single input % IU to all the outputs of the system: % . -1 % x = Ax + Bu G(s) = C(sI-A) B + D % y = Cx + Du RE(w) = real(G(jw)), IM(w) = imag(G(jw)) % % The frequency range and number of points are chosen automatically. % % LNYQUIST(NUM,DEN) produces the Nyquist plot for the polynomial % transfer function G(s) = NUM(s)/DEN(s) where NUM and DEN contain % the polynomial coefficients in descending powers of s. % % LNYQUIST(A,B,C,D,IU,W) or LNYQUIST(NUM,DEN,W) uses the user-supplied % freq. vector W which must contain the frequencies, in radians/sec, % at which the Nyquist response is to be evaluated. When invoked % with left hand arguments, % [RE,IM,W] = LNYQUIST(A,B,C,D,...) % [RE,IM,W] = LNYQUIST(NUM,DEN,...) % returns the frequency vector W and matrices RE and IM with as many % columns as outputs and length(W) rows. No plot is drawn on the % screen. % See also: LOGSPACE,MARGIN,BODE, and NICHOLS. % % J.N. Little 10-11-85 % Revised ACWG 8-15-89, CMT 7-9-90, ACWG 2-12-91, 6-21-92, % AFP 2-23-93 % WCM 8-30-97 % ******************************************************************** Modifications made to the nyquist - takes into account poles on jw axis. then goes around these to make up frequency vector % % if nargin==0, eval('exresp(''nyquist'')'), return, end --- Determine which syntax is being used --- nargin1 = nargin; nargout1 = nargout; if (nargin1==1),% System form without frequency vector [num,den]=tfdata(a,'v'); z = roots(num); p = roots(den); zp = [z;p]; wpos = zp(find(abs(zp)>0)); if(min(abs(p)) == 0) wstart = max(eps, 0.03*min([1;wpos])); else wstart = max(eps, 0.03*min(abs(zp))); end wstop = max([1000;30*wpos]); w = logspace(log10(wstart),log10(wstop),max(51,10*max(size(zp))+1)); %w = freqint2(num,den,30); [ny,nn] = size(num); nu = 1; %error('Wrong number of input arguments.'); elseif (nargin1==2), if(isa(a,'ss')|isa(a,'tf')|isa(a,'zpk')) % System with frequency vector [num,den]=tfdata(a,'v'); w = b; else% Transfer function form without frequency vector num = a; den = b; z = roots(num); p = roots(den); zp = [z;p]; wpos = zp(find(abs(zp)>0)); if(min(abs(p)) == 0) wstart = max(eps, 0.03*min([1;wpos])); else wstart = max(eps, 0.03*min(abs(zp))); end wstop = max([1000;30*wpos]); w = logspace(log10(wstart),log10(wstop),max(51,10*max(size(zp))+1)); %w = freqint2(num,den,30); end [ny,nn] = size(num); nu = 1; elseif (nargin1==3), % Transfer function form with frequency vector num = a; den = b; w = c; [ny,nn] = size(num); nu = 1; elseif (nargin1==4), % State space system, w/o iu or frequency vector error(abcdchk(a,b,c,d)); [num,den] = ss2tf(a,b,c,d); [z,p,k]= ss2zp(a,b,c,d); [num,den] = zp2tf(z,p,k); zp = [z;p]; wpos = zp(find(abs(zp)>0)); if(min(abs(p)) == 0) wstart = max(eps, 0.03*min([1;wpos])); else wstart = max(eps, 0.03*min(abs(zp))); end wstop = max([1000;30*wpos]); w = logspace(log10(wstart),log10(wstop),max(51,10*max(size(zp))+1)); %w = freqint2(a,b,c,d,30); nargin1 = 2;%[iu,nargin,re,im]=mulresp('nyquist',a,b,c,d,w,nargout1,0); %if ~iu, if nargout, reout = re; end, return, end [ny,nu] = size(d); elseif (nargin1==5), % State space system, with iu but w/o freq. vector error(abcdchk(a,b,c,d)); z = tzero(a,b,c,d); p = eig(a); zp = [z;p]; wpos = zp(find(abs(zp)>0)); if(min(abs(p)) == 0) wstart = max(eps, 0.03*min([1;wpos])); else wstart = max(eps, 0.03*min(abs(zp))); end wstop = max([1000;30*wpos]); w = logspace(log10(wstart),log10(wstop),max(51,10*max(size(zp))+1)); %w = freqint2(a,b,c,d,30); [ny,nu] = size(d); else error(abcdchk(a,b,c,d)); [ny,nu] = size(d); end if nu*ny==0, im=[]; w=[]; if nargout~=0, reout=[]; end, return, end ********************************************************************* depart from the regular nyquist program here now we have a frequency vector, a numerator and denominator now we create code to go around all poles and zeroes here. if (nargin1==5) | (nargin1 ==4) | (nargin1 == 6) [num,den]=ss2tf(a,b,c,d); end tol = 1e-6; %defined tolerance for finding imaginary poles z = roots(num); p = roots(den); ***** If all of the poles are at the origin, just move them a tad to the left*** if norm(p) == 0 if(isempty(z)) tad = 1e-1; else tad = min([1e-1; 0.1*abs(z)]); end length_p = length(p); p = -tad*ones(length_p,1); den = den(1,1)*[1 tad]; for ii = 2:length_p den = conv(den,[1 tad]); end zp = [z;p]; wpos = zp(find(abs(zp)>0)); if(min(abs(p)) == 0) wstart = max(eps, 0.03*min([1;wpos])); else wstart = max(eps, 0.03*min(abs(zp))); end wstop = max([1000;30*wpos]); w = logspace(log10(wstart),log10(wstop),max(51,10*max(size(zp))+1)); %w = freqint2(num,den,30); end zp = [z;p]; % combine the zeros and poles of the system nzp = length(zp); % number of zeros and poles ones_zp=ones(nzp,1); %Ipo = find((abs(real(p))<1e-6) & (imag(p)>=0)) %index poles with zero real part + non-neg imag part Ipo = find((abs(real(p)) < tol) & (imag(p)>=0)); %index poles with zero real part + non-neg imag part if ~isempty(Ipo) % **** only if we have such poles do we do the following:************************* po = p(Ipo); % poles with 0 real part and non-negative imag part check for distinct poles [y,ipo] = sort(imag(po)); % sort imaginary parts po = po(ipo); dpo = diff(po); idpo = find(abs(dpo)>tol); idpo = [1;idpo+1]; % indexes of the distinct poles po = po(idpo); % only distinct poles are used nIpo = length(idpo); % # of such poles originflag = find(imag(po)==0); % locate origin pole s = []; % s is our frequency response vector %w = sqrt(-1)*w; % create a jwo vector to evaluate all frequencies with for ii=1:nIpo % for all Ipo poles [nrows,ncolumns]=size(w); if nrows == 1 w = w.'; % if w is a row, make it a column end; if nIpo == 1 r(ii) = .1; else % check distances to other poles and zeroes pdiff = zp-po(ii)*ones_zp; % find the differences between % poles you are checking and other poles and zeros ipdiff = find(abs(pdiff)>tol); % ipdiff is all nonzero differences r(ii)=0.2*min(abs(pdiff(ipdiff))); % take half this difference r(ii)=min(r(ii),0.1); % take the minimum of this diff.and .1 end; t = linspace(-pi/2,pi/2,25); if (ii == originflag) t = linspace(0,pi/2,25); end; % gives us a vector of points around each Ipo s1 = po(ii)+r(ii)*(cos(t)+sqrt(-1)*sin(t)); % detour here s1 = s1.'; % make sure it is a column % Now here I reconstitute s - complex frequency - and % evaluate again. bottomvalue = po(ii)-sqrt(-1)*r(ii); % take magnitude of imag part if (ii == originflag) % if this is an origin point bottomvalue = 0; end; topvalue = po(ii)+sqrt(-1)*r(ii); % the top value where detour stops nbegin = find(imag(w) < imag(bottomvalue)); % nnbegin = length(nbegin); % find all the points less than encirclement if (nnbegin == 0)& (ii ~= originflag) % around jw root sbegin = 0 else sbegin = w(nbegin); end; nend = find(imag(w) > imag(topvalue)); % find all points greater than nnend = length(nend); % encirclement around jw root if (nnend == 0) send = 0 else send = w(nend); end w = [sbegin; s1; send]; % reconstituted half of jw axis end else w = sqrt(-1)*w; end %end % this ends the loop for imaginary axis poles ************************************************************* back to the regular nyquist program here Compute frequency response if (nargin1==1)|(nargin1==2)|(nargin1==3) gt = freqresp(num,den,w); else gt = freqresp(a,b,c,d,iu,w); end *********************************************************** nw = length(gt); mag = abs(gt); % scaling factor added ang = angle(gt); mag = log2(mag+1); % scale by log2(mag) throughout for n = 1:nw h(n,1) = mag(n,1)*(cos(ang(n,1))+sqrt(-1)*sin(ang(n,1))); end; % recalculate G(jw) with scaling factor gt = h; *********************************************************** ret=real(gt); imt=imag(gt); If no left hand arguments then plot graph. if nargout==0, status = ishold; plot(ret,imt,'r-',ret,-imt,'g--') plot(real(w),imag(w)) modifications added here %******************************************* % set(gca, 'YLimMode', 'auto') limits = axis; % Set axis hold on because next plot command may rescale set(gca, 'YLimMode', 'auto') set(gca, 'XLimMode', 'manual') hold on % Make arrows for k=1:size(gt,2), g = gt(:,k); re = ret(:,k); im = imt(:,k); sx = limits(2) - limits(1); [sy,sample]=max(abs(2*im)); arrow=[-1;0;-1] + 0.75*sqrt(-1)*[1;0;-1]; sample=sample+(sample==1); reim=diag(g(sample,:)); d=diag(g(sample+1,:)-g(sample-1,:)); % Rotate arrow taking into account scaling factors sx and sy d = real(d)*sy + sqrt(-1)*imag(d)*sx; rot=d./abs(d); % Use this when arrow is not horizontal arrow = ones(3,1)*rot'.*arrow; scalex = (max(real(arrow)) - min(real(arrow)))*sx/50; scaley = (max(imag(arrow)) - min(imag(arrow)))*sy/50; arrow = real(arrow)*scalex + sqrt(-1)*imag(arrow)*scaley; xy =ones(3,1)*reim' + arrow; xy2=ones(3,1)*reim' - arrow; [m,n]=size(g); hold on plot(real(xy),-imag(xy),'r-',real(xy2),imag(xy2),'g-') end xlabel('Real Axis'), ylabel('Imag Axis') limits = axis; % Make cross at s = -1 + j0, i.e the -1 point if limits(2) >= -1.5 & limits(1) <= -0.5 % Only plot if -1 point is not far out. line1 = (limits(2)-limits(1))/50; line2 = (limits(4)-limits(3))/50; plot([-1+line1, -1-line1], [0,0], 'w-', [-1, -1], [line2, -line2], 'w-') end % Axis plot([limits(1:2);0,0]',[0,0;limits(3:4)]','w:'); plot(-1,0,'+k'); if ~status, hold off, end % Return hold to previous status return % Suppress output end %reout = ret; % plot(real(p),imag(p),'x',real(z),imag(z),'o'); 


Published with MATLAB® 9.2

Control Tutorials for MATLAB and Simulink (2024)

FAQs

How do I find answers in MATLAB? ›

To view all of your solutions, go to a Problem page and click View my solutions. You can view your solutions in a list or in the Solution Map. If using the list view, you can review the display by selecting a Sort by option.

Is MATLAB Simulink hard to learn? ›

MATLAB is designed for the way you think and the work you do, so learning is accessible whether you are a novice or an expert. The Help Center is always available to guide you with robust documentation, community answers, and how-to videos. Additionally, online interactive training is a great way to get started.

How much time is required to learn MATLAB? ›

If you're a novice programmer, you can expect it to take a little longer than if you were a more seasoned programmer. Someone who can afford to devote all their time to MATLAB can finish learning the language in two weeks. If you have a lot of other responsibilities, however, it will take you longer to complete.

Does anyone still use MATLAB? ›

As of May 2022, LinkedIn searches return about 7.6 million Python users and 4.1 million MATLAB users. People who do not work in engineering or science are often surprised to learn how widespread MATLAB is adopted, including: Millions of users in colleges and universities. Thousands of startups.

How do I find Solver in MATLAB? ›

Description. S = solve( eqn , var ) solves the equation eqn for the variable var . If you do not specify var , the symvar function determines the variable to solve for. For example, solve(x + 1 == 2, x) solves the equation x + 1 = 2 for x.

Is MATLAB harder than Python? ›

The Difference in Technical Computing:

They are both used for the same type of work, such as numerical analysis, data visualization, and scientific computation. When it comes to syntax and readability, Python is often easier to read and understand than MATLAB.

What is the salary of MATLAB Simulink? ›

Average Annual Salary by Experience

Matlab Developer salary in India with less than 1 year of experience to 5 years ranges from ₹ 2.0 Lakhs to ₹ 9.4 Lakhs with an average annual salary of ₹ 5.6 Lakhs based on 342 latest salaries.

Is MATLAB enough for a job? ›

Conclusion. The industry has some familiar buzz that learning MATLAB will not be a good opportunity for a better career. But this is not fully true. Yes, it is an acceptable reason that salary or company structure will never be able to touch available popular jobs on other programming technologies.

Is MATLAB in high demand? ›

Matlab careers are actually on the rise today. It's a very popular programming language. It can be used by a developer, engineer, programmer, scientist, etc. to collect and sort out data, and develop apps, software, and sites.

Can I learn MATLAB in 1 month? ›

If you want to become an expert in Matlab then you need to mention which part of madlab you want to learn and want expertise. If I generalize my answer highly, It may take at least 3 months to learn matlab and may take maximum 3 years to become an expert.

Is MATLAB becoming obsolete? ›

MATLAB is almost dropping off from the top 20 for the first time in more than a decade. In April 2021, it was at the 19th position, and now, a year after that, it has dropped further. MATLAB finds its usage in the numerical analysis domain and is often combined with Simulink.

Does NASA use MATLAB? ›

In 2022, the team at NASA published a report titled “Rapid Flight Control Law Deployment and Testing Framework for Subscale VTOL Aircraft”, describing flight control law development and deployment using UAV Toolbox with MATLAB.

Is MATLAB losing to Python? ›

Is MATLAB better than Python? 🔗 Almost always, no. For the vast majority of readers, Python is the better choice because it's free to use and get started with, the libraries make it a more versatile language, and it's just a better language for data science, machine learning, software development, and programming.

How to check results in MATLAB? ›

View Results in Command Window

The Summary Report link provides access to the Model Advisor Command-Line Summary report. You can review additional results in the Command Window by calling the DisplayResults parameter when you run the Model Advisor.

How do you find the step response in MATLAB? ›

[ y , tOut ] = step( sys , tFinal ) computes the step response from t = 0 to the end time t = tFinal . [ y , tOut ] = step( sys , t ) returns the step response of a dynamic system model sys at the times specified in the vector t .

How do I find something in MATLAB code? ›

Search Using Find Dialog Box

The Find dialog box opens. The search begins at the current cursor position. MATLAB finds the text you specified and highlights it. MATLAB beeps when a search for Find Next reaches the end of the Command Window, or when a search for Find Previous reaches the top of the Command Window.

What is the ANS command in MATLAB? ›

ans is the variable created when an output is returned without a specified output argument. MATLAB® creates the ans variable and stores the output there. Changing or using the value of ans in a script or function is not recommended, as the value can change frequently. ans is specific to the current workspace.

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