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Future Perspectives of PID
                 Controllers
                     (industrial process control)




          IFAC Conference on Advances in PID Control
                 Brescia, 28-30 March 2012
                                                       Willy Wojsznis




Slide 1                IFAC - PID’12 – Brescia Italy
Thoughts
          • Where PID is?

          • Why PID ?

          • Direction for evolution

          • Examples of PID evolution

          • Conclusion


Slide 2

                      IFAC - PID’12 – Brescia Italy
Where PID is?

              PID proved it can compete in many
               applications with new promising techniques,
               like
              Fuzzy Logic
              Model based controllers
              MPC
              Slogans “replace all PID” is not used as10-20
               years ago


Slide 3
                         IFAC - PID’12 – Brescia Italy
Where PID is ?


          •   PID “found” its favorite spot, where it is doing the
              better than other techniques

             This is the low and intermediate level control in
              process industry where PID is absolutely dominant
              control

             MPC reign in multivariable control and optimization.
              PID provide good control at the lower level


             Instead of competition – good cooperation
Slide 4
                            IFAC - PID’12 – Brescia Italy
Why PID ?
          •   Feedback is universal control

          •   Intuitive for the human, appreciated by operators

          •   P   I   D - the most natural rules

          •   PID like control used
          in nature on various levels
          and time scale
          (molecular level
          - seconds,
          species – thousand
           of years)
Slide 5
                               IFAC - PID’12 – Brescia Italy
PID – direction for evolution
             Enhanced PID for dealing with special conditions or
              applications with added logic and calculations

             Examples: saturated conditions, wireless, event driven,
              non-linear ….

             Robust adaptive tuning and control

             Performance monitoring and reporting

             Valve diagnostics – mechanical failure can nullify all gains
              achieved from improved tuning or control strategy



Slide 6
                             IFAC - PID’12 – Brescia Italy
PID at saturated conditions
          •   A better response to major upsets can be achieved through the
              use of a dynamic pre-load and reducing the filtering that is
              applied in the positive feedback path when the output limited




Slide 7
                            IFAC - PID’12 – Brescia Italy
PID – model based adaptive tuning
             Why model based adaptation?
                                                   Model validation for model switching
                                                    adaptation with parameter interpolation is
                                                    performed in parallel with parameter
                                                    evaluation


                                                   Ratio of maximum to minimum errors
                                                    signifies how fast is conversion


                                                   If the model with the middle parameter value
                                                    has smallest error it indicates the optimum is
         Well established, intuitive tuning
          rules – Lambda, IMC, SIMC                 within adaptation range
         Model can be used for other
          purposes – loop diagnostics,             Statistical validation – recent model quality,
          performance monitoring….
                                                     parameters standard deviation, number
                                                    of adaptations
Slide 8
                                IFAC - PID’12 – Brescia Italy
PID – model free adaptation
             There are number of smart techniques model free
              techniques
                                                PVi(t                              SPi(t)
             Fictitious set point              )                 +            +
                                                  OUTi(t
                                                  )
             Controller switching

             Balancing controller terms
                                                                         P
                          P   Pk ; and I   I k ;              
                                 k                            k          I
                                                1 
                           Ti (k )   Ti (k )   1
                                                 
Slide 9
                              IFAC - PID’12 – Brescia Italy
PID – loop diagnostics

              Valve diagnostics features – mechanical failure
               can nullify all gains achieved from improved
               tuning or control strategy




Slide 10
                           IFAC - PID’12 – Brescia Italy
PID – loop diagnostics


          Simple valve diagnostics can detect valve dead
           band and hysteresis

                 h  2 A(out )                2 Ampl ( PV )  Kr


                r  2 Ampl ( PV )                   b  hr
                                    K



Slide 11
                         IFAC - PID’12 – Brescia Italy
PID future - conclusion
              PID will continue to be main control on the basic
               level in the process industry


              PID evolution as discussed will enhance PID
               competitiveness


              Model free adaptive tuning can be useful in
               special applications




Slide 12
                           IFAC - PID’12 – Brescia Italy

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Future Perspectives of PID Control

  • 1. Future Perspectives of PID Controllers (industrial process control) IFAC Conference on Advances in PID Control Brescia, 28-30 March 2012 Willy Wojsznis Slide 1 IFAC - PID’12 – Brescia Italy
  • 2. Thoughts • Where PID is? • Why PID ? • Direction for evolution • Examples of PID evolution • Conclusion Slide 2 IFAC - PID’12 – Brescia Italy
  • 3. Where PID is?  PID proved it can compete in many applications with new promising techniques, like  Fuzzy Logic  Model based controllers  MPC  Slogans “replace all PID” is not used as10-20 years ago Slide 3 IFAC - PID’12 – Brescia Italy
  • 4. Where PID is ? • PID “found” its favorite spot, where it is doing the better than other techniques  This is the low and intermediate level control in process industry where PID is absolutely dominant control  MPC reign in multivariable control and optimization. PID provide good control at the lower level  Instead of competition – good cooperation Slide 4 IFAC - PID’12 – Brescia Italy
  • 5. Why PID ? • Feedback is universal control • Intuitive for the human, appreciated by operators • P I D - the most natural rules • PID like control used in nature on various levels and time scale (molecular level - seconds, species – thousand of years) Slide 5 IFAC - PID’12 – Brescia Italy
  • 6. PID – direction for evolution  Enhanced PID for dealing with special conditions or applications with added logic and calculations  Examples: saturated conditions, wireless, event driven, non-linear ….  Robust adaptive tuning and control  Performance monitoring and reporting  Valve diagnostics – mechanical failure can nullify all gains achieved from improved tuning or control strategy Slide 6 IFAC - PID’12 – Brescia Italy
  • 7. PID at saturated conditions • A better response to major upsets can be achieved through the use of a dynamic pre-load and reducing the filtering that is applied in the positive feedback path when the output limited Slide 7 IFAC - PID’12 – Brescia Italy
  • 8. PID – model based adaptive tuning  Why model based adaptation?  Model validation for model switching adaptation with parameter interpolation is performed in parallel with parameter evaluation  Ratio of maximum to minimum errors signifies how fast is conversion  If the model with the middle parameter value has smallest error it indicates the optimum is  Well established, intuitive tuning rules – Lambda, IMC, SIMC within adaptation range  Model can be used for other purposes – loop diagnostics,  Statistical validation – recent model quality, performance monitoring…. parameters standard deviation, number of adaptations Slide 8 IFAC - PID’12 – Brescia Italy
  • 9. PID – model free adaptation  There are number of smart techniques model free techniques PVi(t SPi(t)  Fictitious set point ) + + OUTi(t )  Controller switching  Balancing controller terms P P   Pk ; and I   I k ;  k k I 1  Ti (k )   Ti (k )   1   Slide 9 IFAC - PID’12 – Brescia Italy
  • 10. PID – loop diagnostics  Valve diagnostics features – mechanical failure can nullify all gains achieved from improved tuning or control strategy Slide 10 IFAC - PID’12 – Brescia Italy
  • 11. PID – loop diagnostics  Simple valve diagnostics can detect valve dead band and hysteresis h  2 A(out ) 2 Ampl ( PV )  Kr r  2 Ampl ( PV ) b  hr K Slide 11 IFAC - PID’12 – Brescia Italy
  • 12. PID future - conclusion  PID will continue to be main control on the basic level in the process industry  PID evolution as discussed will enhance PID competitiveness  Model free adaptive tuning can be useful in special applications Slide 12 IFAC - PID’12 – Brescia Italy