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

• Sampling process

• Signal aliasing

• Sample rates

• Holding process

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

Actuators

• Motors– Field and series wound

– AC and DC

– Stepper

– PWM

Actuators

• Valves

• Pumps

• Heaters

• Smart materials

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

Sensors

• Optical encoders– Decoding– Absolute and relative– Resolution

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

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/software level