Introduction to MATLAB

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# Introduction to MATLAB - PowerPoint PPT Presentation

Introduction to MATLAB. Chris Diduch University of New Brunswick January 31 – February 4, 2004 Royal Bhutan University RBIT, Rinchending Day-5. Robot Kinematics Animation. Robot Kinematic Animations. Build 3D cylinder shapes for each link

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Presentation Transcript

### Introduction to MATLAB

Chris Diduch

University of New Brunswick

January 31 – February 4, 2004

Royal Bhutan University

RBIT, Rinchending

Day-5

Robot Kinematic Animations
• Build 3D cylinder shapes for each link
• The homogenous transformation specifies a rotation and translation of points
• An end effector trajectory is generated by interpolating between points
• Change the rotation and translation of each link as governed by the next interpolated point
• Redraw or update the figure display
Graphical User Interfaces
• GUI’s are attached to figure windows
• Push buttons
• Check boxes
• Text boxes
• Sliders
• ….
GUI for the Pendulum
• Add a Pause and End push button
• Add a Slider and Text box for changing and displaying the pendulum length parameter
Day 5 Proposed Topics
• Symbolic math toolbox
• Control systems toolbox
• Identification toolbox
• Signal processing toolbox
Symbolic Math Toolbox
• Manipulate and solve symbolic equations
• MAPLE (licensed by Mathworks) is the underlying engine
• Graphical entry tool for dynamic systems
• A dynamic model relating outputs to inputs
• May use Simulink for simulation
• With other toolboxes (Real time workshop, xPC Target …)
• May interface to data acquisition hardware
• May be compiled and executed under a real time operating system
• Supports multiple targets
Control System Toolbox
• System representation in many forms:
• Transfer function
• State space
• Pole–zero
• Step, impulse and transient response
• Bode, Nyquist, Nichols, pole-zero plots
• Statefeedback
• Pole placement
• LQG
• Model order reduction
RLC Circuit

L

vin

vout

C

R

+

-

vp

vo

vn

R2

C

R1

u

y

Excitation

Model

Identification Toolbox

System Identification Toolbox

u

y

fft()

fft()

uf

yf

Nonparametric Model
Identification Algorithms
• Spectral analysis, spa()
• Predictive error method, pem()
• Autoregressive, ar()
• Instrumental variables, iv4()
• Autoregressive moving average, arma()
• Box-Jenkins, bj()
Signal Processing Toolbox
• Filtering and FFT’s
• Signals representation
• Time and frequency response
• IIR and FIR filter analysis and design
• Statistical signal processing
• Correlation and covariance
• Spectral analysis
• Windowing
• Cepstrum analysis
Digital Filters and Correlation
• Input, u, is selected as a pulse
• Plot u and filter output, y
• Plot FFT of input pulse, u
• Plot FFT of filter output, y
• Plot autocorrelation of input pulse, u
• Plot autocorrelation of filter output, y
Final Summary