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EMK310 Theory Lecture 5 Output control methods Professor Tania Hanekom

EMK310 Theory Lecture 5 Output control methods Professor Tania Hanekom. References Ball chapter 5 Additional material as provided Your own reading. Microprocessor-based control. Real-world device is controlled to match a desired value

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EMK310 Theory Lecture 5 Output control methods Professor Tania Hanekom

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  1. EMK310 TheoryLecture 5Output control methodsProfessor Tania Hanekom • References • Ball chapter 5 • Additional material as provided • Your own reading

  2. Microprocessor-based control • Real-world device is controlled to match a desired value • Digital control system can produce output that is complex function of the input • Sensor is sampled – make sure sample rate is adequate

  3. Open loop control • No feedback • No certainty if device being controlled is doing what it is supposed to be doing • Example: A motor is switched on or off Processing (PIC code) Input signal Output device

  4. Negative feedback • Output is fed back to input to ensure that desired output/balance is reached. Processing (PIC code) Input signal Output device

  5. On/off control • Only 2 states – with feedback • Issues: • Overshoot – inertia/process speed • Oscillation – a result of overshoot • Dead band (hysteresis) can be included to prevent these issues.

  6. Proportional control • Vary control based on difference between desired signal and actual signal where error e = set-point – actual value and M is an offset • As error decreases, output decreases, e.g. heater approaches set-point.

  7. Proportional control: issues • Best used in static environment • Changing conditions are not considered and could cause problems: • e.g., automobile cruise control • headwinds, uphill and downhill, horsepower decrease as a result of aircon could require different control parameters

  8. Proportional, integral, derivative (PID) control • History of system is added as another input • Reflects reaction of system to previous change where I is the amount of integral to apply and D is the amount of derivative to apply

  9. PID control [2] • Derivative term • Derivative is measure of how fast the error is changing • Rate of change in error gives indication of the size of the load • Allows output to • rapidly respond to changing inputs • compensate for varying loads

  10. PID control [3] • Integral term • Accumulation of errors • Long-term control parameter • Pushes the output towards the set-point if PD control settled at offset from set-point. • Proportional term • Causes output to follow input

  11. Model Predictive Control • Uses mathematical model of system to be controlled to determine outputs • Parameters are frequently changed dynamically • Could require much memory

  12. Control techniques summary GLR • Two sensors at sides of colour sensor. • Left detects line > go right • Right detects line > go left • Array of sensors • Left sensor array > fright(nr left sensors) • Right sensor array > fleft (nr right sensors)

  13. Microprocessor-based control systems: issues summary • Complexity: Output is a function of input, history, rate of change, type of load, etc. • Sampled system • Measurement delay (e.g. thermistor) • Process delay (e.g. heater) • Controlled object delay (e.g. thermal mass) • Imperfect coupling between sensor and controlled object • Overshoot and oscillation • Saturation / windup (PI and PID) • Noise (derivative term) • Discontinuous input

  14. Self study • Motor control (pp 127-133) • Measurement and analysis (pp134-138) • Tutorials • Pseudocodeexamples (pp 138-142) • Things to remember (p 142) (NB!!)

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