This document discusses self-tuning controllers and internal model control (IMC). It describes self-tuning controllers as controllers where the parameters in the process model are updated online as new data is acquired, allowing the controller settings to adapt. IMC is presented as a control design technique that uses an internal model of the process to design a controller that is stable and robust even with process changes. The key aspects of IMC include specifying a process model, model uncertainty, input type, and performance objective. The document outlines the basic IMC control structure and how it works to track setpoints and reject disturbances.

1. ICE401: PROCESS INSTRUMENTATION
AND CONTROL
Class 38
Self Tuning Controllers and IMC
Dr. S. Meenatchisundaram
Email: meenasundar@gmail.com
Process Instrumentation and Control (ICE 401)
Dr. S.Meenatchisundaram, MIT, Manipal, Aug – Nov 2015

2. Adaptive Control Systems:
Process Instrumentation and Control (ICE 401)
Dr. S.Meenatchisundaram, MIT, Manipal, Aug – Nov 2015
• An adaptive control system is one in which the controller
parameters are adjusted automatically to compensate for changing
process conditions.
• Many adaptive control techniques have been proposed for
situations where the process changes are largely unknown or
unpredictable, as contrasted with situations amenable to the gain-
scheduling approach.
• Many such controllers are referred to as self-tuning controllers or
self-adaptive controllers.

3. Self Tuning Controllers:
Process Instrumentation and Control (ICE 401)
Dr. S.Meenatchisundaram, MIT, Manipal, Aug – Nov 2015
• In self-tuning control, the parameters in the process model are
updated as new data are acquired (using on-line estimation
methods), and the control calculations are based on the updated
model.
• For example, the controller settings could be expressed as a function of
the model parameters and the estimates of these parameters
updated on-line as process input/output data are received.
• Self-tuning controllers generally are implemented as shown in Fig.

4. Self Tuning Controllers :
Process Instrumentation and Control (ICE 401)
Dr. S.Meenatchisundaram, MIT, Manipal, Aug – Nov 2015

5. Self Tuning Controllers:
Process Instrumentation and Control (ICE 401)
Dr. S.Meenatchisundaram, MIT, Manipal, Aug – Nov 2015
• In Fig., three sets of computations are employed:
estimation of the model parameters,
calculation of the controller settings, and
implementation of the controller output in a feedback loop.
• Most real-time parameter estimation techniques require that an
external forcing signal occasionally be introduced to allow
accurate estimation of model parameters.
• Such a perturbation signal can be deliberately introduced through the
set point or added to the controller output.
• During each disturbance or set-point change, the process response
is compared to the predicted model response, and then the model can
be updated based on the prediction error.

6. Internal model control (IMC):
Process Instrumentation and Control (ICE 401)
Dr. S.Meenatchisundaram, MIT, Manipal, Aug – Nov 2015
• Internal model control (IMC), which is based on an accurate model of
the process, leads to the design of a control system that is stable and
robust.
• A robust control system is one that maintains satisfactory control in
spite of changes in the dynamics of the process.
• In applying the IMC method of control system design, the following
information must be specified:
• Process model
• Model uncertainty
• Type of input (step, ramp, etc.)
• Performance objective (integral square error, overshoot, etc.)

7. Internal Model Control Structure:
Process Instrumentation and Control (ICE 401)
Dr. S.Meenatchisundaram, MIT, Manipal, Aug – Nov 2015

8. Internal Model Control Structure:
Process Instrumentation and Control (ICE 401)
Dr. S.Meenatchisundaram, MIT, Manipal, Aug – Nov 2015

9. Internal Model Control Structure:
Process Instrumentation and Control (ICE 401)
Dr. S.Meenatchisundaram, MIT, Manipal, Aug – Nov 2015
• If the model exactly matches the process (i.e., Gm=G ), the only signal
entering comparator 1 in Fig. is U1. (The signals from G and Gm
are equal and cancel each other in going through comparator 2).
• Since U1 is not the result of any processing by the transfer functions
in the forward loop, U1 is not a feedback signal but an independent
signal that is equivalent to R in its effect on the output C.
• In fact, there is no feedback when G = Gm, and we have an open-loop
system as shown in Fig.
• In this case the stability of the control system depends only on GI and
Gm. If GI and Gm are stable, the control system is stable.

10. Internal Model Control Structure:
Process Instrumentation and Control (ICE 401)
Dr. S.Meenatchisundaram, MIT, Manipal, Aug – Nov 2015
• Ideally, we should like to have C track R without lag when only a set
point change occurs (i.e., U1 =0).
• For this to occur, we see from previous Fig. or Eq. that G1 G = 1, or
since G = Gm, we may write GIGm = 1. Solving for GI gives
• The above equation simply states that the IMC controller should be the
inverse of the transfer function of the process model.

11. Internal Model Control Structure:
Process Instrumentation and Control (ICE 401)
Dr. S.Meenatchisundaram, MIT, Manipal, Aug – Nov 2015
• IMC Design Procedure
• IMC Design Example
• Refer Page No. 381, Process Systems and Analysis by Donald R
Coughanowr, Third Edition.

12. References:
• http://www.enggcyclopedia.com/2012/06/split-range-control-loop/
• http://www.controleng.com/single-article/a-dual-split-range-control-
strategy-for-pressure-and-flow-
processes/e02afa4eb60717657598546e8feb895e.html
Process Instrumentation and Control (ICE 401)
Dr. S.Meenatchisundaram, MIT, Manipal, Aug – Nov 2015