Mechatronics at the University of Calgary: Concepts and Applications Jeff Pieper.
The Mechatronics Design Lab Course at the University of Calgary Presented June 2, 2003.
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Transcript of The Mechatronics Design Lab Course at the University of Calgary Presented June 2, 2003.
Mechatronics Systems Design
• What is mechatronics?
• What have we learned?
• What can I do with this?
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
Mechatronics
• Complex decision making in physical systems– Control
– Power and information flow
– Implies higher complexity than pure mechanical systems possible
What have we learned?
• Filter design and analysis
• Sampled-data systems behaviour
• Mechanical systems interfacing
• Feedback control design and limitations
Filters
• Analog and digital
• Design for signal attenuation and amplification
• Characteristics and behaviour
Filters
• Choice of design:– Mechanical components
– Analog circuits
– Digital electronics
– Software
Sampled-data systems
• Limits on sampling rates:
– High –> hardware limits
– Low –> replication of signal limits
Mechanical Systems
• Actuators and sensors
• Data acquisition and control (DAQ or DAC)
• Software
• Hardware
Sensors
• Voltage• Displacement
– potentiometers
• Temperature– Thermocouple– Thermistor– RTD– Hot wire anemometer
Sensors
• Pressure– Capacitive– Strain gauge
• Stress– Strain gauge
• Acceleration and velocity– Accelerometer and tachometer
Data acquisition and control
• Software and interface
• Sampling rates– Continuous– Discrete
• Filtering
• Calibration
Feedback Control
• PID– Continuous versus discrete– Steady state error– Lead/lag filters and PID– P, PI, PD or PID design choice– Anti-windup
Feedback Control
• Lead compensation– Stability margin: gain and phase margins
• Q-parameterization– All internally stabilizing controllers
• Actuator saturation
Feedback Control
• State space systems– State feedback– Linear quadratic optimal control– Choice of weighting parameters
• State estimators– Linear quadratic estimators
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
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
Student’s 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
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