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

Is MATLAB good for control systems? ›

As a control systems engineer, you can use MATLAB® and Simulink® at all stages of development, including plant modeling, controller design, deployment with automatic code generation, and system verification.

Where can I learn MATLAB Simulink? ›

  • MathWorks. Robotics Education - MATLAB and Simulink Robotics Arena. ...
  • MathWorks. Mechatronics with MATLAB and Simulink. ...
  • MathWorks. Simulink Onramp. ...
  • MATLAB/Simulink for the Absolute Beginner. 1894 ratings at Udemy. ...
  • Learn MATLAB and SIMULINK in one week. 555 ratings at Udemy. ...
  • IIT Roorkee; NPTEL. ...
  • MathWorks. ...
  • MathWorks.

How to simulate control system in MATLAB? ›

The first step is to define the system that you want to simulate. You need to specify the system parameters, such as the transfer function, the state-space model, the input and output variables, and the initial conditions. You can use MATLAB commands or graphical tools to define the system.

What is Simulink used for in MATLAB? ›

Simulink is the platform for Model-Based Design that supports system-level design, simulation, automatic code generation, and continuous test and verification of embedded systems. Key capabilities include: A graphical editor for modeling all components of a system.

What is the control system toolbox in MATLAB? ›

Control System Toolbox™ provides algorithms and apps for systematically analyzing, designing, and tuning linear control systems. You can specify your system as a transfer function, state-space, zero-pole-gain, or frequency-response model.

Which engineers use MATLAB the most? ›

Mechanical engineers of Design and manufacturing field use MATLAB and Simulink heavily.

Is MATLAB more powerful than Python? ›

MATLAB's optimized numerical computing engine allows it to outperform Python in certain mathematical operations. However, Python, with libraries like NumPy built on efficient C and Fortran code, significantly narrows the performance gap.

Which software is better than MATLAB? ›

1. GNU Octave. If you are looking for anything closer to Matlab in terms of compatibility and computational ability, then Octave is the best Matlab alternative. Most of the projects developed for Matlab run on Octave too.

Why use Simulink instead of MATLAB? ›

Another factor to consider when choosing between Simulink blocks and MATLAB code is the speed and efficiency of your system. Simulink blocks can be faster and more efficient for some tasks, such as prototyping, testing, and debugging.

How long does it take to learn MATLAB Simulink? ›

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.

Do I need to know MATLAB for Simulink? ›

Simulink is for MATLAB Users

Use MATLAB and Simulink together to combine the power of textual and graphical programming in one environment. Apply your MATLAB knowledge to: Optimize parameters. Create new blocks.

How to design a controller in MATLAB Simulink? ›

To design a controller, first select the controller sample time and horizons, and specify any required constraints. For more information, see Choose Sample Time and Horizons and Specify Constraints. You can then adjust the controller weights to achieve your desired performance. See Tune Weights for more information.

Does MATLAB have a circuit simulator? ›

Simulate Model and Analyze Results

In the model window, select Simulation > Run to run the simulation. To view the triangle wave in the Scope window, double-click the Scope block. You can do this before or after you run the simulation. This plot shows the voltage waveform.

Can MATLAB be used to control a robot? ›

MATLAB provides several built-in algorithms and functions for robot programming. For example, with just a few lines of out-of-the-box deep learning algorithms in MATLAB, robots can identify objects in the environment.

What is the command for controllability in MATLAB? ›

Co = ctrb( A , B ) returns the controllability matrix Co using the state matrix A and input-to-state matrix B . The system is controllable if Co has full rank, that is, the rank of Co is equal to the number of states.

How to use source control in MATLAB? ›

To set up a project with source control, use any of these workflows:
  1. Create a new project from an existing repository.
  2. Add an existing project to source control.
  3. Create a new project in a folder already under source control.
  4. Create a new GitHub® repository for a new or an existing project.

How does a control system work? ›

A control system is a set of mechanical or electronic devices that regulates other devices or systems by way of control loops. Typically, control systems are computerized. Control systems are a central part of production and distribution in many industries. Automation technology plays a big role in these systems.

How to design a controller in MATLAB? ›

To design a controller, first select the controller sample time and horizons, and specify any required constraints. For more information, see Choose Sample Time and Horizons and Specify Constraints. You can then adjust the controller weights to achieve your desired performance. See Tune Weights for more information.

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