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Project Completion. ECE 496 Fall 2002 Gyrobot Team D. Outline. The Group/Team The Project Design Specifications Design Approach Main Model / Encoders Balance Control Swing-up Control Hardware Challenges Software Challenges Performance Summary Questions. Team.
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Project Completion ECE 496 Fall 2002GyrobotTeam D
Outline • The Group/Team • The Project • Design Specifications • Design Approach • Main Model / Encoders • Balance Control • Swing-up Control • Hardware Challenges • Software Challenges • Performance • Summary • Questions
Team • Ray Price – Team Leader • David Epting – Hardware Designer / Webmaster • John Abbott -- Hardware Designer / Presentation Manager • Matt Vaughn – Lead Software Designer / Electronics Technician • Cyrus Griffin – Software Designer / Photographer
The Project • The Gyrobot is an underexcited pendulum, consisting of a single link (arm) with a flywheel driven by a dc motor mounted at the free end • The Gyrobot was to include a control algorithm that uses the generated inertia of the flywheel to cause the pendulum to invert and balance with the flywheel at the 12 o’clock Position
Project Specifications • The Gyrobot had to fit the following criteria: • Must comply to the mechanical specification of thesis by Adrian Jenkyn Lee out of the University of Illinois at Urbana-Champaign. • Must utilize motor/flywheel inertia to invert pendulum and then balance. • Utilizes Simulink RTW controller.
Design Approach • Specified / developed hardware • Procured hardware • Assembled Gyrobot • Tested interfaces (encoders and analog output) • Compiled Encoder / Main Routine • Compiled Balance Routine and Tested • Compiled Swing-up Routine and Tested
Design Approach • Gantt Chart
Main Software Model The main software model combined the encoders, swing-up and balancing algorithms.
Position Encoder Used to produce arm position (theta\1 from 0 to 2pi and a theta1 from –pi to pi) also produced the arm velocity theta1dot.
Motor Encoder Converts number of swings to radian and filters to produce a theta2dot—the flywheel velocity.
Software • Pd Balance Control - The Model
Balance Control • Important Variables: • Arm position (theta 1) • Arm velocity (theta1dot) • Flywheel velocity (theta2dot)
Balance Control • Arm Position • Added enough energy to move mass of assembly to the highest position--Fighting gravity • Gain of kp = 3.375 was used based on center of gravity and mass of the mobile assembly (motor, flywheel, arm, shaft)
Balance Control • Arm Velocity • As the arm approached vertical it should slow. • Arm must be able to fight acceleration if it falls away from vertical. • Gain of kd = .72 based on rotational inertia of the whole mobile system.
Balance Control • Flywheel Velocity • The flywheel stopped when the arm is balancing. • Gain of k = .0006 was small, in effect creating an under-damped system. • Gain is negative 175 (after the summer) to bring the speed of the flywheel to zero (instead of slowly ramping up).
Swing Up Control • Sinusoidal model from thesis was used because: • Smoother • Faster due to harmonics • Less bouncing in controls compared to other proposed methods
Swing Up Control • Sends a sinusoidal signal to the motor • Motor switches polarity via a switch when the arm reaches zero velocity • Theoretically the control effort is supposed to slow down as balance is approached, but since we saturated the effort this doesn’t really happen
Mechanical Design Challenges • Flywheel • Problems • Flywheel was not properly centered during milling process • Wheel would wobble and eventually flew off • Solution • A glue was applied along with the set screw • The glue absorbed most of the vibration, drastically reducing the wobble
Mechanical Design Challenges • Pittman Motor • Problems • While pressing the flywheel onto the motor the encoder was pushed off-center • Heat generated during use led to inconsistent performance • Solution • A new motor was ordered in exchange for the damaged one and the flywheel was attached by a set screw instead of being pressed on • Followed a set timing schedule
Mechanical Design Challenges • Bearings • Problems • Bearings were too stiff, generating un-needed friction • Solution • Bearings eventually loosened up after use
Control Challenges - Balance • Problem: Parameter Optimization • Solution: Optimize only 1 control variable at a time
Control Challenges - Balance • Problem: Limited Pull-up ability • Solution: Create a window outside of which the routine does not engage.
Control Challenges – Swing-up • Problem: Recovery time between runs. • Solution: Use a 4-swing swing-up. Allow cooling time.
Control Challenges - Swingup • Problem: Inconsistent effort window. • Solution: Set window each day based on the temperature of the room, and the temperament of the gyrobot.
Control Challenges - Transition After a repeatable swing-up was established, there were still problems with the transition to balance. • Problem: Inconsistent room temperature. • Solution: Practice the routine enough times on competition day to get a “feel” for cooling time.
Performance • Final Competition • Fastest time – 2.47 seconds (2nd Place) • Bonus Day • 9 out of 10
Summary • Were able to build a gyrobot device and the associated control structure that would invert and balance the gyrobot pendulum • Able to balance and resist impulsive forces against the device • Able to “swing-up” in 4 swings.
