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Best Practices for Control Loop Optimization

Control Loop Optimization means improving the performance of control loops to get the best possible performance from them. The improvement task is often attempted in an ad-hoc or trial-and-error fashion, but this is mostly ineffective and seldom results in truly optimal loop performance. Effective control loop optimization is done in a systematic way, by following the best practices.

  1. Know Your Process. This item seems almost too obvious to be on the list, but it is often tempting to address a control problem through tuning without considering the broader process. Process knowledge provides guidance on the control objective, tuning rules to use, diagnostic tests to do, and the process conditions under which to do the tuning. Things to know about the process include: the process type (integrating or self-regulating), ratio of process lag to dead time, if the process’ gain or dynamic characteristics might change under varying operating conditions, type of final control element being used and its flow characteristics, disturbances to the process and if they are measurable, possible negative side-effects from process-variable overshoot or a rapidly changing controller output.
  2. Determine the Control Objective.  Consider the following: Should the loop perform fast or slow? Is overshoot tolerable? Should the controller output move a little as possible? Does the controller’s set point often change? Does the loop have to compensate for process disturbances? The control objective will dictate the type of tuning method to use. The control objectives could be fast setpoint tracking or fast disturbance rejection (of which each could have sub-objectives such as minimum absolute error or minimum integral of error), zero process-variable overshoot, a specific process response to setpoint changes, minimum controller output movement, and no overshoot in the manipulated variable. Surge tank level loops, for example, should be tuned to minimize controller output movement while keeping the level between predefined limits.
  3. Review the Control Strategy. Review the design of the control strategy with the aid of process and instrumentation diagrams. Does the design support the control objective established above? Are cascade, feedforward, ratio, and other control strategies required and applied correctly? Are there interactive control loops? If so, how is that being handled? The control strategy should support the control objective, given the broader process with its disturbances, nonlinearities, and other nuances. For example, a simple feedback control loop will do an awful job if ratio control is actually needed. Cascade control should be used only if the inner loop is much faster than the outer loop. Feedforward control should be used to compensate for process disturbances, except when the disturbances directly affect the flow rate through the final control element – requiring cascade control. When done correctly, control strategies can significantly contribute to control loop stability and responsiveness. Unfortunately, the opposite is also true.
  4. Do a Plant Walk-Down. Inspect the size and layout of the process equipment and the condition and location of the instrumentation and final control element (i.e., valve, damper, or variable speed pump). Is everything in good condition and located in the right place? Considering the size of the equipment, you should be able to get some sense of how fast or slow the process will respond to controller output changes. This knowledge will help when doing step-testing.
  5. Examine the Measurement Device. Ensure the process measurement is good for the application. Is the transmitter ranged appropriately? Is this the best sensing technology for the process conditions? Is the device installed correctly?
  6. Evaluate the Use of Filtering. Check if transmitter dampening or process variable filtering is being used. Transmitters should use an anti-aliasing filter, but no more. Filtering, if required, should be done in the control system to simplify its adjustment and facilitate replacing the transmitter without having to worry about filtering. Inspect a time trend of the process variable and determine if filtering is required, and how much. If a process variable filter is used, its time constant should be reviewed to ensure that it is set appropriately and significantly shorter than the dominant process time constant.
  7. Test the Final Control Element. An improperly working final control hurts control loop performance and can negate proper controller tuning methods. Typical problems include deadband, stiction, a nonlinear flow curve, and positioner problems. These problems may appear very similar to tuning problems, and an unknowing tuner may spend many hours of futile tuning if the problem lies with the control valve. A few simple process tests should be done to detect and diagnose final control element problems before any tuning is attempted. These problems will have to be resolved for optimal control performance. Also, final control element problems can significantly skew results from process tests and cause the calculation of completely incorrect tuning parameters.
  8. Review the Controller Configuration. Modern, digital controllers offer a range of options to optimize their performance for various situations. Setpoints can be ramped or filtered internally to obtain a smooth control response even when the operator makes an abrupt change. Setpoint changes can also be hidden from the proportional and derivative control modes. External reset feedback prevents integral windup under adverse conditions, and rate-of-change limits can protect sensitive equipment downstream. Check the controller algorithm and configurable controller options before tuning the controller.
  9. Use an Appropriate Tuning Method. Contrary to popular belief, controller tuning is much more science than art. Loop tuning can be done quickly and accurately based on the control objective, process characteristics, and appropriate tuning rules. Process characteristics can be determined by making a step-change in controller output and taking measurements from the resulting process response. Although trial-and-error tuning is popular, it should be used only as a last resort, for example with processes that are so volatile that it is impossible to get usable step-test data. As an alternative to manually calculating tuning parameters based on step-test results, loop tuning software offers many helpful features such as identification of process characteristics, producing tuning settings for different tuning objectives, providing simulations of anticipated loop response, analyzing control loop robustness, and more. However, tuning software is only a tool, and someone incapable of manually tuning controllers using step-test data and tuning rules will likely also find it difficult using tuning software.
  10. Tune from Multiple Step Tests. Simulations may be 100% repeatable, but real processes are not. Process disturbances, interacting control loops, nonlinearities, and operating conditions can all affect measured process characteristics. Tuning from only one step test can result in poor tuning settings if the process response at that instance was not normal for whatever reason. It is essential to do multiple step tests to obtain “average” measurements of process characteristics, and an appreciation of how much they change under normal conditions.
  11. Cater for Nonlinearities and Changing Process Characteristics. The installed flow characteristic of a final control element is often not linear. In addition, the characteristics of many processes change under different process conditions (production rates, equipment in service, catalyst concentration, pH, etc.). Control valves and dampers might have to be linearized using a characterizer, and changing process characteristics might require the scheduling of controller parameters (called gain scheduling or adaptive tuning).
  12. Validate and Test the New Values. Compare the newly calculated controller settings with the ones in the controller, and ensure that any large differences in numbers are expected and justifiable. Implement and test the new controller settings. Ensure the controller is tuned to work in harmony with the dynamics of the process it is controlling, and meeting the overall control objective of the loop. First let the loop settle out and evaluate its performance under steady conditions. Does it oscillate? Does the controller output move too much? If the loop should respond to setpoint changes, make a setpoint change and look for overshoot, oscillations, or too much controller output movement. If the loop should respond to disturbances, briefly put the controller in manual, change the output by a few percent, and immediately put the controller back in auto. This simulates a disturbance. Again, check for unnecessary overshoot, oscillations, excessive controller output movement. Monitor the controller’s performance periodically for a few days after tuning to verify improved performance under different process conditions.
  13. Keep Records. Make note of the previous controller settings, the new settings, and the date and time of change. You should keep a computerized or paper-based log of all changes to a control loop. Leave the previous controller settings with the operator in case he/she wants to revert back to them and cannot find you to do it. If the new settings don’t work, you have probably missed something in one or more of the practices above.

If the desired control objectives cannot be met by following these practices, including repairing faulty equipment and making changes in control strategy, model-predictive control can be investigated as a possible solution. The final and perhaps most expensive alternative would be to modify the process equipment, but this is rarely needed. In the majority of cases the solution is available in the practices listed above.

Stay tuned!

Jacques Smuts – Author of the book Process Control for Practitioners

 

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