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PID to Model Predictive Control

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25 diapositives · 1.2K Views · Aug 14, 2014

PID to Model Predictive Control. Yurong Kimberly Wang Adjunct Professor - OIT. Objectives. Industrial process control challenge PID control limitation Model predictive control development procedures MPC for operation advantage MPC vendors and reference materials.

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PID to Model Predictive Control
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  1. PID to Model Predictive Control Yurong Kimberly Wang Adjunct Professor - OIT

  2. Objectives • Industrial process control challenge • PID control limitation • Model predictive control development procedures • MPC for operation advantage • MPC vendors and reference materials

  3. Tyco Electronics / Precision Interconnect

  4. Coax Manufacturing Processes • Dielectric layer • Taping or Extrusion • Shield layer • Braiding or Serving • Jacket layer • Taping or Extrusion

  5. Coax Property • Formulae • C: capacitance (pF/Foot) • Td: time delay (ns/Foot) • Z0: impedance (Ohm) • k: dielectric constant • D: outer diameter (Mil) • d: center conductor diameter (Mil)

  6. Process Control Challenge • Multiple Outputs • Capacitance, Diameter, Time delay, Impedance, … • Multiple Inputs • Screw speed, line speed, barrel temperatures, tape tensions, … • Long and Variable Time Delays • Variable line speeds and sensor to actuator distances • Input and Output Constraints • Input and output upper and lower spec limits • Nonlinearity • Variety of operating conditions • Disturbances • Center conductor variation, tape thickness variation, …

  7. PID Control Limitation • Multiple-loop PID with decoupling • Cascade PID loops • Gain scheduling PID • Anti-windup for input constraints • Difficult to control large time delay processes • Difficult to control non-minimum phase processes

  8. Model Predictive Control (MPC) • Model-based multi-variable control • Optimal control law with I/O constraints • Nonlinear control with model mismatch • Long and variable time delay processes • Non-minimum phase processes

  9. MPC System and Optimization

  10. MPC Sampling Instants Tuning parameters: prediction horizon and control horizon

  11. Process Modeling Tools • Models based on first principals • Mechanics, thermodynamics, heat transfer, fluid dynamics, … • S or Z domain or state space models • Models based on system identification • Step response method: TF, FIR, BJ, ARX, ARMAX,… • PRBS method: TF, FIR, BJ, ARX, ARMAX,… • MatLAB System Identification Toolbox • LabVIEW System Identification Toolkit

  12. Simulation Tools • MatLAB and Simulink • LabVIEW Control Design and Simulation Module • Tuning parameters • Output weights: the higher the weight, the closer the output to setpoint • Input weights: the higher the weight, the closer the input to setpoint • Input change weights: the higher the weight, the slower the response • Predictive horizon: up to plant settling time • Control horizon: case specific for each control objective

  13. MPC scrap PID scrap PID and MPC Setpoint Following Case 1: time delay is 41 steps

  14. PID and MPC Setpoint Following Case 2: time delay is 82 steps

  15. MPC with Predictive SP Case 3: setpoint profile is used

  16. Production Production Quality Engineers Plant Managers Process Engineers Manufacturing Engineers Production Production Production Production Report Program for Data Analysis Business Network Remote Users Internet Manufacturing Information Server OPC Client & Server for Data Logging Business Network LabVIEW HMI & MPC Control OPC Client & Server for Data Sharing Control Network Figure 1. System Architecture Local Control Module Local Control Module Local Control Module Local Control Module LabVIEW MPC System Architecture

  17. LabVIEWTM MPC Project

  18. LabVIEW MPC Block Diagram

  19. MatLABTM MPC Block Diagram

  20. LabVIEW MPC Application

  21. MPC for Operation Advantage • Six Sigma process performance and optimal product quality control • Multi-variable auto-controlled product quality with constraints • Productivity improvement • Unmanned auto production overnight run and throughput ramp up • Equipment cost reduction • Inner diameter gauge elimination • Sensor fault detection • Controller acting up with sensor fault readings • Labor cost reduction • Coax off-line test and operator/machine ratio reduction

  22. MPC Vendors • Aspen Technology • Honeywell • Emerson Process Management • Siemens • Shell Global • MathWorks • National Instruments • …

  23. Reference Material • A survey of industrial model predictive control technology, Control Engineering Practice, 2003 by S. J. Qin, and T. A. Badgwell • Advanced Control Unleashed, ISA, 2003 by Terrence L. Blevins, Gregory K. McMillan, Willy K. Wojsznis, and Michael W. Brown • LabVIEW Model Predictive Control Module User Manual, 2009 by National Instruments • MatLAB Model Predictive Control Toolbox User Manual, 2009 by MathWorks • MatLAB System Identification Toolbox User manual, 2009 by MathWorks

  24. Conclusion • MPC for complicated process controls • MPC development procedures • MPC for operation advantage • MPC vendors and reference materials

  25. Question and Answer • Contact information yurongwang1@yahoo.com • “A survey of industrial model predictive control technology” website: http://cepac.cheme.cmu.edu/pasilectures/darciodolak/Review_article_2.pdf • “Advanced Control Unleashed” website: http://www.isa.org/Template.cfm?Section=Books1&template=Ecommerce/FileDisplay.cfm&ProductID=6087&file=Adv.ControlUnleashed_TableofContents.pdf