The key attributes of the overall performance of any controller are robustness, set-point tracking, and disturbance rejection. The primary objective of control system design therefore is the design and implementation of controllers that attain a reasonable degree of success in each of these three attributes. Thus, for all control schemes—from regulatory control using the classical PID algorithm to the more advanced supervisory control using model predictive control—the objective is to select tuning parameters that result in good set-point tracking and disturbance rejection without sacrificing robustness. In the specific case of PID controllers, however, the tuning parameters are not related to these critical attributes directly; designing the controller to achieve desired performance in each of these attributes is therefore not straightforward. The reason for such limitations is that the PID algorithm is an early 1900’s concept that is reflective of the hardware implementation technology of the time. Remarkably, the modern digital PID controller remains primarily a digital realization of this century-old technology. The PID controller, used in over 80% of industrial loops, therefore still possesses weaknesses that limit its achievable performance, and an intrinsic structure that makes tuning more complex and less transparent than necessary. The literature on PID controller tuning is extensive, and significant research effort is still being expended on the design of this class of controllers.
We have recently argued for the need to develop an alternative regulatory controller that can be designed and implemented more transparently, and proposed the RTDA controller as one such alternative to the PID controller. This new control scheme combines the simplicity of the PID controller with the versatility of Model Predictive Control (MPC) but with more transparent tuning than is associated with both. The controller tuning parameters, θR, θT, θD, and θA, are related directly to the controller performance attributes of robustness, set-point tracking, disturbance rejection and overall controller aggressiveness, respectively; they are all naturally scaled between 0 and 1, leading to a controller that can be designed and implemented much more directly and transparently. We have also developed tuning rules for the controller, based on robust stability analysis in which, for a given plant/model mismatch, stability regions are characterized as functions of the controller parameters.
We will present in this seminar a derivation of the controller technique and its tuning rules; we will then illustrate its performance with several examples including a simulated nonlinear polymerization reactor along with actual implementation on a laboratory-scale water tank system and on a pilot-scale physical vapor deposition process for manufacturing flexible materials for photovoltaic cells.