The Mechatronics Design Lab Course at the University of Calgary

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The Mechatronics Design Lab Course at the
University of Calgary

• Presented June 2, 2003

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Mechatronics Systems Design

• What is mechatronics?
• What have we learned?
• What can I do with this?

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Mechatronics

• Some Definitions
• Synergistic integration of mechanics,
  electronics, computation and control.
• Control of power flow in electro-mechanical
  systems
• Complex decision making in physical systems

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Mechatronics

• Complex decision making in physical systems
• Control
• Power and information flow
• Implies higher complexity than pure mechanical
  systems possible

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What have we learned?

• Filter design and analysis
• Sampled-data systems behaviour
• Mechanical systems interfacing
• Feedback control design and limitations

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Filters

• Analog and digital
• Design for signal attenuation and amplification
• Characteristics and behaviour

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Filters

• Choice of design
• Mechanical components
• Analog circuits
• Digital electronics
• Software

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Sampled-data systems

• Sampling process
• Signal aliasing
• Sample rates
• Holding process

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Sampled-data systems

• Limits on sampling rates
• High gt hardware limits
• Low gt replication of signal limits

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Mechanical Systems

• Actuators and sensors
• Data acquisition and control (DAQ or DAC)
• Software
• Hardware

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Actuators

• Motors
• Field and series wound
• AC and DC
• Stepper
• PWM

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Actuators

• Valves
• Pumps
• Heaters
• Smart materials

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Sensors

• Voltage
• Displacement
• potentiometers
• Temperature
• Thermocouple
• Thermistor
• RTD
• Hot wire anemometer

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Sensors

• Pressure
• Capacitive
• Strain gauge
• Stress
• Strain gauge
• Acceleration and velocity
• Accelerometer and tachometer

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Sensors

• Optical encoders
• Decoding
• Absolute and relative
• Resolution

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Data acquisition and control

• Software and interface
• Sampling rates
• Continuous
• Discrete
• Filtering
• Calibration

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Feedback Control

• PID
• Continuous versus discrete
• Steady state error
• Lead/lag filters and PID
• P, PI, PD or PID design choice
• Anti-windup

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Feedback Control

• Lead compensation
• Stability margin gain and phase margins
• Q-parameterization
• All internally stabilizing controllers
• Actuator saturation

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Feedback Control

• State space systems
• State feedback
• Linear quadratic optimal control
• Choice of weighting parameters
• State estimators
• Linear quadratic estimators

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What can I do with this?

• We have examined most of the sub-stages in a
  feedback control loop
• Actuators
• dynamics system
• sensors
• controllers
• software and user interface
• hardware and computer systems interface

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What can I do with this?

• We have applied this to as variety of mechanical
  systems
• Motors
• Motors plus ball and beam, gantry crane
• Thermal systems
• Electronics

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Students Final Projects

• State estimation of inverted pendulum system
• Optimal controller for inverted pendulum system
• Regenerative braking system model using Simulink
  and State Flow
• Actuator saturation in control methods
• System identification of a flexible link using
  frequency response techniques

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What I learned

• Advanced control theories and their applications
• Experience with open ended problems in control
• Exposure to a laboratory setting, useful for
  students exploring the idea of grad studies
• Extensive use of the MatLAB and Simulink
  computing environments

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Key points

• Some important ideas that you can use
• Software and programming are key
• Sampling
• information flow
• Dynamic system details
• Reconfigurability via software portability leads
  to economic advantage
• Design choices are at mechanical/electronic/softwa
  re level