Digital Control Systems

Links

Textbooks

• • Introduction to Applied Digital Control, Greg Starr, University of New Mexico
    • Greg's ME481/ME581 web pages contain solutions to the book problems and other things.
  • Optimal Sampled-Data Control Systems, Chen & Francis
  • Digital Control Systems Notes Irfan Ahmed
  • Digital Control Engineering Fadali & Visioli
  • Discrete Time Control Systems Ogata
  • Control System Engineering, Chapter 13 (Digital Control) Norman Nise
  • Digital Control Systems Notes Ben Chen
  • Computer Control Systems
  • Digital Control System Analysis and Design, 3rd Ed., Philips
  • Control Systems and Control Engineering
  • Introduction to Signal Processing
  • Zoran Gajic's Book on Control Systems (Continuous & Discrete) and some other documents. Look for the documents entitled Chapter N.
  • NPTEL Course on Digital Control

Lectures

• 2014
  • Sampled Time Control
  • Duy-Ky Nguyen's Concise Digital Control Notes I like this presentation.
  • Zoran Gajic's notes on Observers. This is a very short set of lecture notes that explains the why's of observers, with several interesting derivations.
  • Professor Bemporad's Automatic Control Class Notes These are very good.
    • Professor Bemporad's presentation on the separation principle of state space regulator design (showing that the poles of the control setting are preserved even when using a full order observer).
  • MIT Class on Underwater Vehicle Control
    • Linear Quadratic Regulators (Mathematically Deep)
    • A Mathematically Involved, but Insightful Look at Kalman Filters. It has some interesting remarks about the duality between the Kalman-Bucy Filter for continuous systems and the LQR algorithm for continuous systems. I expect the discrete versions are also duals.
  • Kalman Filter Applied to Cart in One Dimensional Motion
  • Intuitive Lecture on Practical Kalman Filter given by Francis Grovers III
  • The first 80% of what you need to know about Kalman Filters from Greg Czerniak.
  • Introduction the the Kalman Filter and its Derivation, Brent Perreault
  • A Simple Introduction to the Kalman Filter (This reference from Duy-Ky Nguyen has some excellent examples and very readable derivations.)
• Papers
  • David Luenberger's Introduction to Observers
  • F.L. Lewis' LQR Explanations
    • F.L. Lewis Linear Quadratic Regulator Derivation and Examples
    • F.L. Lewis Discrete LQR Derivation and Examples This goes nicely with the above paper doing it for continuous time.
  • Jake Abbott's lecture on the Linear Quadratic Regulator
  • Greg Welch & Gary Bishop's Introduction to Kalman Filters
  • Maria Isabel Ribeiro paper on Kalman Filters
  • The Descriptor State Space Model
  • Relation Between Continuous Differential Equations and Discrete Difference Equations
• 2016
  • Equations of Motion for a DC Motor
  • Controllable and Modal Canonical Forms of the State Space Representation

MATLAB/Octave

• Octave Control Systems Toolbox This is not the same thing that is on Octave Forge here.
• Digital Control Octave Scripts

Pendulum Specific Scripts

Single Pendulum

% Double Pendulum Parameters (Tentative:  There are two pendulum setups, each with different parameters.  I'm not sure which these go to.)
% This script is for balancing only the long rod.

% Run parameters
%f = input('Control Frequency (Hz) = ');
%Trun = input('Run Time (s) = ');
%f=130;
f=1000;
Trun=30;

kmax = round(f*Trun);
T = 1/f;
Maxpos = 0.25;              % Max carriage travel +- 0.25 m
Maxangle = 0.175;           % Max rod angle -- 10 deg
Maxvoltage = 20;            % Max motor voltage, V
pstart = 0.005;             % Carriage position starting limit, m
astart = 1*pi/180;          % Angle starting limit, rad

g = 9.81;                   % m/s^2     Gravitational constant

% SYSTEM PARAMETERS
% Measured Mechanical Parameters
d1 = 0.323;                 % m         Length of pendulum 1 (long)
d2 = 0.079;                 % m         Length of pendulum 2 (short)
%mp1 = 0.0208;              % kg        Mass of pendulum 1
mp1 = 0.0318;
%mp2 = 0.0050;              % kg        Mass of pendulum 2
mp2 = 0.0085;
%m=.5;
m = 0.3163;                 % kg        Mass of carriage
rd = 0.0254/2;              % m         Drive pulley radius
md = 0.0375;                % kg        Mass of drive pulley (cylinder)
%mc1 = 0.0036;              % kg        Mass of clamp 1*
%mc2 = 0.0036;              % kg        Mass of clamp 2*
mc1 = 0.0085;
mc2 = mc1;

% *Clamp Dimensions
%  Rectangular 0.0254 x 0.01143 m
%  The pivot shaft is 0.00714 m from the end

% Motor Parameters (Data Sheet)
Im = 43e-7;                 % kg m^2/rad    Rotor moment of inertia
R = 4.09;                   % ohms            Resistance
kt = 0.0351;                % Nm/A            Torque constant
ke = 0.0351;                % Vs/rad        Back emf constant

% Derived Mechanical Parameters

                            % kg m^2/rad    Moment of inertia, clamp 1
%Ic1 = mc1*(0.01143^2 + 0.0254^2)/12 + mc1*(0.0127-0.00714)^2;
Ic1 = mc1*(0.0098^2 + 0.0379^2)/12;
Ic2 = Ic1;                  % kg m^2/rad    Moment of inertia, clamp 2
Id = md*(rd^2)/2;           % kg m^2/rad    Moment of inertia, drive pulley
Imd = Im + Id;              % kg m^2/rad    Moment of inertia, combined

J1 = Ic1 + mp1*(d1^2)/3;    % Total moment of inertia, pendulum 1 (long)
J2 = Ic2 + mp2*(d2^2)/3;    % Total moment of inertia, pendulum 2 (short)
Jd = Im + Id;               % Total moment of inertia, motor drive
Mc = m + mc1 + mc2;         % Total carriage mass

% Friction Test Data
%   Carriage Slope = 19 deg;  Terminal Velocity xdotss = 0.312 m/s; From
%       twincarriage.m; formula b = m g sin(theta)/xdotss
%   Pendulum 1 (long) Exponent a1 = 0.0756 1/s;  From longfit.m
%   Pendulum 2 (short) Exponent a2 = 0.2922 1/s; From shortfit.m
%        formula b = 2 a J

%alpha = 19;
alpha = 12.2;
%xdotss = 0.312;
xdotss = 0.4852;
%a1 = 0.0756;
%a2 = 0.2922;
a1 = 0.0185;
a2 = 0.012;
                            % Ns/m    Viscous friction of carriage system
b = (Mc + mp1 + mp2)*g*sin(alpha*pi/180)/xdotss;
b1 = 2*a1*J1;               % Nms/rad    Viscous friction of pendulum 1 (rotational)
b2 = 2*a2*J2;               % Nms/rad    Viscous friction of pendulum 2 (rotational)

scale = [rd*2*pi/4096  2*pi/4096 -0.05/250];


T = 1/f;

% The data above comes from the fweb wiki.

M=Mc;                       %mass of cart
m=mp1;                      %mass of pendulum 1
b=b;                        %friction
l=d1/2;                     %length of pendulum
I=J1;                       %inertia of pendulum
%q=(M+m)*(l+m*l^2)-(m*l)^2;
%num=[m*l,0];                                %numerator for transfer function
%den=[q,b*(l+m*l^2),-m*g*l*(M+m),-b*m*g*l];  %denominator for transfer function
%[A,B,C,D]=tf2ss(num,den)
%A,B,C,D matricies for the state space model
% x_vec is [x,  x_dot, theta, theta_dot]'
% See the web site:  https://www.library.cmu.edu/ctms/ctms/examples/pend/invpen.htm
A=[ 0   1                                                  0                                   0;
    0   ((-(I+m*l^2)*b)/(I*(M+m)+M*m*l^2))  ((m^2*g*l^2)/(I*(M+m)+M*m*l^2))     0;
    0   0                                   0                                   1;
    0   ((-m*l*b)/(I*(M+m)+M*m*l^2))        ((m*g*l*(M+m))/(I*(M+m)+M*m*l^2))   0];
B=[ 0;
    ((I+m*l^2)/(I*(M+m)+M*m*l^2));
    0;
    ((m*l)/(I*(M+m)+M*m*l^2))];
C=[ 1   0   0   0;
    0   0   1   0];
D=[ 0;
    0];
cont_sys = ss(A,B,C,D)
rank_ctrb = rank(ctrb(A,B))
original_poles = eig(A) %poles for our system

These Pendulum Parameters and control scripts are for our hardware.

Z Transforms

• Relationship between the Laplace and Z transforms
• Convolution and Z Transforms
  • Z Transforms and Convolution
  • Here is an animation of convolution with continuous signals. Look at it so you understand what is happening with the mechanics of convolution. To convolve $x(t)$ with $h(t)$, you flip shift $h(t)$ into $h(t-t_{0})$ on the $t_{0}$ axis, then you multiply it by $x(t_{0})$ to get $x(t_{0})h(t-t_{0})$, then you integrate with respect to $t_{0}$, so that the convolution is: $x(t)*h(t)=\int _{-\infty }^{\infty }x(t_{0})h(t-t_{0})dt_{0}$. The first animation shows this happening with sampled waveforms: $x_{s}(t)=x(t)\sum _{n=0}^{\infty }\delta (t-nT)=\sum _{n=0}^{\infty }x(nT)\delta (t-nT)$ and $h_{s}(t)=h(t)\sum _{n=0}^{\infty }\delta (t-nT)=\sum _{n=0}^{\infty }h(nT)\delta (t-nT)$.
    • Discrete convolution animations
      • Here is an animation of discrete convolution to illustrate the above equations.
      • This one lets you try several functions.
      • This one shows what happens if your signals happen prior to time zero.
  • Notice that this is really the same as Polynomial Multiplication.

Discretization

• This is how the c2d zero order hold works in MATLAB/octave. It uses the "exact" solution to the discretization problem.
• Also check Chapter 7.3 of Greg Starr's book.

Scilab/Xcos

• Xcos Beginners Guide
• Scilab/Scicos Book