Emerson Exchange 365

Over in LinkedIn’s Process Control group, a question was asked : What is the difference between regulatory and model predictive control. Is it possible that MPC work as stand alone without regulatory control? Emerson’s Chowyang Neo offered: Chowyang Neo Sales & Marketing Manager Regulatory Control generally refers to good old PID loop control. Gaps between the SP and PV known as errors are being fed to a PID controller so that Process Variables (PV) is then being steered closer towards the setpoint (SP) based on the magnitude of the error. There can be more advanced variants of this simple PID – cascade, ratio, feedforward etc. But all of these are generally considered as Regulatory or Adv Regulatory Control. The use of model based technique is driven more by the complex, multi-variable nature of some of the processes we see in this industry. The level control of boiler is a classic example with many factors playing a part…. Have a read to get more Emerson’s Lou Heavner added: Lou Heavner Systems/Project Engineering Consultant There is a hierarchy of process control starting with open loop, where the operator is the controller. He makes manual adjustments to valves and other final control elements to keep the process at operating targets or within operating constraints. This represents the absence of automation. Next up the hierarchy there are feedback controllers that take the place of the operator automatically closing the loop, with PID being the most prevalent by far. This basic level of feedback control is inherently single loop in nature. There is one measured variable that is controlled and one manipulated variable (usually a control valve) that is the process input. A control panel or DCS may have hundreds of single-input-single-output feedback control loops for the operator to manage. The next level up the hierarchy involves a branch. Up one path, there is supervisory control that is used to sequence the process or manage a batch process. That is not the realm of MPC, although MPC may be seen in batch and sequential processes. The other branch includes multivariable regulatory control. One approach to multivariable control is advanced regulatory control in which single control loops may be cascaded, ratioed, or included as part of complex strategies with feed forward inputs to cancel measured disturbances and override controls to handle constraints. Special features in digital systems also make it easy to handle nonlinearity with characterization, create inferential calculations, and a host of other functions to improve control. Modified PID controllers such as Smith Predictor or “Error Squared” or other controller algorithms can be used to deal with deadtime, nonlinearity, and other challenges. Occasionally you may see a feedback controller that is not PID per se, but uses something like fuzzy logic. A specific type of advanced control is model predictive control. This type of controller uses dynamic process models and... Read the full text.