Transcript Document
MICYCLE
A self-balancing electric unicycle Andrew Kadis David Caldecott Andrew Edwards Matthew Haynes Miroslav Jerbic Rhys Madigan Supervisor: Assoc. Prof. Ben S. Cazzolato Co-Supervisor: Dr. Zebb Prime
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Introduction Submitted paper focused on developing the system dynamics and simulating them The control response of the simulated and physical systems were then compared This presentation has a slightly different focus, concentrates on the wider Micycle system
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Literature review
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Concept development Incorporation of steering mechanism Extensive research into steering mechanisms Use of a rotary damper
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Concept development (2) Lego Mockup Preliminary Concept Model
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Components Microcontroller Motor controller Sensor Power supply Motor
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Mechanical design goals Chassis assembly Damper Spring Fork Protective rubber Perspex covers Steering mechanism Assembly of Micycle
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Major mechanical components Chassis assembly Simple plate chassis design Protective Perspex covers Protective rubber Fork design Rotary damper drive Offset centre for motor Dual bearing design Chromoly steel Plate chassis Perspex covers Chassis plate assembly Protective rubber Damper drive Bearing locations Offset centre Fork
Steering mechanism
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Uses a torsion spring and rotary damper Makes the Micycle much easier to ride Allowed steering angle ±15˚ Steering mechanism
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Mechanical design approach ProE CoG Analysis Iterate design Drafting ANSYS Workbench Structural analysis
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BATTERY Electrical system overview MICRO CONTROLLER DISTRIBUTION BOARD MOTOR CONTROLLER IMU HUB MOTOR PERIPHERALS
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Controller design Self-Balancing Unicycle Mechanical System Electrical System
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Controller structure PD controller structure used Derivative signal taken directly from the IMU rather than differentiated to minimise latency in the sensor readings
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System Dynamics The Lagrangian approach of deriving the system dynamics was applied The dynamics were derived in terms of: φ – the rotation of the frame about the z-axis θ – the rotation of the wheel relative to the z axis Full details can be found in the paper Developed simulation in Simulink from these dynamics
Controller benchmarking - methodology
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Needed a methodology to produce repeatable results to benchmark control system Attached a PD controller with same gains to simulated dynamics Constrained the wheel Point of comparison between physical and simulated control systems to examine response to disturbances Micycle with the wheel constrained
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Controller benchmarking - results Response of simulated system released from 30º Response of physical system released from 30º
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Software functionality
Core Safety Peripheral
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Fall
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Failure modes and effect analysis (FMEA) Comprehensive, iterative process System engineering tool Both a high and low level FMEA performed Over 100 different cases considered Full FMEA is approx. 30 pages long Failure Mode Effect Mitigating Strategies Cause
Safety Control Safety
Andrew Kadis - Software
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Error codes
Safety Trip
Battery drained Vehicle speed too fast Excessive current through motor Pitch position outside safe range Angular velocity too fast General operational failure in the Maxon ADC outside expected bounds IMU did not initialise correctly Maxon did not initialise correctly IMU - abnormal power rating IMU - RS232 pin disconnected IMU - parity check failed IMU - indeterminate communication error
7 Segment Error Code
0 1 2 8 9 A B C 3 4 5 6 7
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Project outcomes Designed, tested and built the Micycle A fully rideable self-balancing electric unicycle which can be learnt to ride in 30 minutes to an hour Comprehensive iterative FMEA process completed 8 hour battery life Significant exposure to the wider community
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Community exposure
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Community exposure (2)
Future work
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Use of a more powerful motor controller to reduce the chances of actuator saturation Implementation of a model based controller Incorporation of active control in the roll direction
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Questions
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