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DOUBLE ARM JUGGLING SYSTEM Progress Presentation ECSE-4962 Control Systems Design

DOUBLE ARM JUGGLING SYSTEM Progress Presentation ECSE-4962 Control Systems Design. Group Members: John Kua Trinell Ball Linda Rivera. Introduction. Where are we? Bulk of Design and Build Complete Testing and Tuning Phase Preliminary Results Physical Design Model Development

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DOUBLE ARM JUGGLING SYSTEM Progress Presentation ECSE-4962 Control Systems Design

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  1. DOUBLE ARM JUGGLING SYSTEM Progress Presentation ECSE-4962 Control Systems Design Group Members: John Kua Trinell Ball Linda Rivera

  2. Introduction • Where are we? • Bulk of Design and Build Complete • Testing and Tuning Phase • Preliminary Results • Physical Design • Model Development • Control Systems Development • Camera Development

  3. Physical Design • Additions: • Camera Mounting • Overall System Mounting • Other physical modifications • Shaft Mounting • Cable extensions • Challenges: • Net Design • Material • Building • Possible solutions: • Foil wrapping current nets • Replacing nets • h Camera Mounting Shaft Mounting System Mounting

  4. Model Development • Lagrange-Euler Model • Single Joint

  5. Simulink Model - Nonlinear

  6. DAC to Current Model • Digital to Analog Conversion • Tested voltage over a range • Fit curve to data – found slope, offset • Voltage to Current Conversion • Adjusted gain to approximately 0.1A/V • Tested current over a range • Applied load to system for accurate measurement

  7. Friction Identification • Identify Viscous and Coulomb Friction • Apply constant torque and measure steady state velocity • Automate with LabVIEW • Process data with MATLAB

  8. Other Parameters • Inertia/Mass • Calculated with SolidWorks • Shaft Spring Constant • Possible cause of oscillations • Experimentally measured • Found to be very stiff - k=4600N/m

  9. Model Linearization • Discard Coulomb Friction • State Space Equations • Transfer Function

  10. Model Verification • Compare Friction ID results to simulated “Friction ID” • Compare simulated controlled output to implemented output • Potentially apply “chirp” ID methods

  11. Velocity Estimation • Finite difference method – 10ms • Minimum velocity of 0.1534 rad/sec • Maximum motor speed of 21 rad/sec • Designed peak velocity of 15 rad/sec • Overflow problem • Seeing large velocity pulse in data • Limited position to +/- 180 degrees • Corrected velocity when over limits (153 rad/sec)

  12. Trajectory Calculation • Drag force on the ball • Trajectory deviates from standard projectile motion equation • Differential Equation • Iterative vs. Simulink ODE Solver

  13. Control Systems Development • Two methods for designing controllers used • MATLAB rltool (Pole Placement method) • 1. Obtain transfer function • 2. Import transfer function to rltool • 3. Convert continuous model to discrete model (sampling time 10ms) • 4. Define design constraints, such as rise time and settling time • 5. Place gain constant at the crossings of design constraints • 6. Export controller to simulink model of system • 7. Run simulation to test • PID block MATLAB (simulink) • Kp = Proportional • Ki = Integral • Kd = Derivative

  14. Pole Placement Methods Tilt-System Root Locus Design Criteria • Closed Loop poles Stable • Locate system poles at the • intersection of ωn and ς ωn = 62.8 rad/s ς = .7

  15. Kp = 800 Ki = 20 Kd = 40 Non-Linear System Step Response To different controllers rltool controller PID controller Overshoot: 28.7% Overshoot: 0%

  16. Camera Development • Vision Module Familiarization: • Use of NI Vision Assistant • Acquire Preliminary data • Carry out a number of tests • Image ProcessingExamples: • Projectile motion launch

  17. Upward vertical launch Processing Challenges: • Blurred images of the ball • Colored backgrounds similar to ball’s color Image 9/30 Image 12/30

  18. Ball blur 1. 2. 3.

  19. Background similar to ball color • Possible solutions: • Blur Take average of circles • Background Create uniform dark background

  20. Data Verification: • Verify if height prediction data is valid • Run new experiment • Compare results • If results from script seem reasonable • Use Overhead camera only • If results form script are unreliable • Add Additional camera on the side • Next Steps: • Running Trajectory Prediction • Integration of Vision Development with Control System • Continue to validate data

  21. Summary of Progress • Schedule • On track, only a few items outstanding • Costs • 10% overbudget, 20% under estimates • Did not purchase motor, built support structures • Plan of Action • No deviations • New Difficulties

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