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Understanding Processor-Based Control Systems: An In-Depth Guide to PID Control

This comprehensive guide explores processor-based control systems, focusing on the integration of microprocessors in modern control applications. We discuss the definition and components of control systems, distinguishing between open-loop and closed-loop systems, and the importance of feedback for effective control. PID (Proportional-Integral-Derivative) control systems are highlighted, showcasing their mechanics and effectiveness in various applications. Also featured are insights into control theory including state variables, error minimization, transient responses, and system stability. Enhance your understanding of these critical systems in engineering.

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Understanding Processor-Based Control Systems: An In-Depth Guide to PID Control

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  1. 4: Processor-based Control Systems CET360Microprocessor Engineeringby J. Sumey

  2. Control Systems • def: collection of mechanical and electrical devices that controls the operation of a physical plant • requires ≥1 output devices (open loop) and≥1 input devices (closed loop) • originally: elec. circuits & mech. devicesnow: µP-based with control algorithm in software

  3. “Control Theory”† • various models • finite state machines • fuzzy logic / neural networks • linear systems • PID (classical) • our main interest here is with PID † http://en.wikipedia.org/wiki/Control_theory

  4. Block Diagram • text…

  5. Components - 1 1. Real state variables, X(t) – properties of the physical plant being controlled • requires sensor(s) and state estimator to produce estimated state variables, X’(t) 2. Desired state variables, X*(t) – what the system seeks for X(t)

  6. Components - 2 3. Control outputs, U(t) – output commands to devices that affect the physical plant typically control actuators which produce driving forces, V(t) 4. Control algorithm – generates U(t) based on error: E(t) = X*(t) – X’(t) goal of a control system: minimize E(t) 6

  7. Control System Effectiveness • determined by 3 properties: 1. steady-state error: average value of E(t) 2. transient response: how long it takes system to reach 99% of final output after X’ or X* is changed 3. stability: if a steady state is reached after a change in X’ or X* • i.e., no oscillations

  8. Open-loop Systems • no feedback, no state estimator • examples: • toaster – fixed amount of time • traffic light – ditto. Must be closed-loop if goal is to maximize traffic flow! • OL stepper motor controller – no encoder 8

  9. Closed-loop Systems includes feedback/state estimator examples: temperature control – add heat or not (bang bang), assumes passive heat loss can add hysteresis with 2 set points: TH, TL robot arm incremental control – U(t) is incremented, decremented, or left alone 9

  10. PID Systems from linear control theory faster, more accurate, better control 10

  11. PID Implementation • break U(t) into separate terms & convert to discrete time • discrete time: fixed period, like DSP (Δt) U(t)=P(t) + I(t) + D(t) P(t)=kP·E(t)  P(n)=kP·E(n)

  12. PID Implementation • noise-prone, better to use average of 2 derivatives over different time spans

  13. References • PID process control, a “Cruise Control” example • http://www.codeproject.com/Articles/36459/PID-process-control-a-Cruise-Control-example • PID Theory • http://pcbheaven.com/wikipages/PID_Theory • PID Controller • http://en.wikipedia.org/wiki/PID_controller

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