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  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 = 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 (2 nd 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.

Questions?