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