(710e) Data-Based Monitoring and Retuning of Low-Level PID Control Loops

Model predictive control (MPC) is widely used in industry because of its ability to handle input/state constraints and to incorporate optimization considerations in a single formulation. In general, in the calculation of the optimal input trajectories for the manipulated inputs via MPC, the dynamics of the corresponding control actuators that will implement the control actions computed by the MPC are neglected  and the MPC-computed control actions are assumed to be directly implemented by the control actuators. However, in practice, these control actuators have their own specific dynamics. As a result of this, there are always discrepancies (i.e., time lags, magnitude differences, etc.) between the actual control actions applied to the process by the control actuators and the actions requested by the MPC. To mitigate the influence of these discrepancies in closed-loop performance, PID controllers (typically called "low-level" PID controllers) are usually implemented on the control actuators to regulate the outputs of the actuators at the values requested by the MPC. In this case, the tuning of the PID controllers is critical for the overall control actuator and closed-loop system performance. An actuator with a well-tuned PID controller can effectively implement the actions requested by the MPC; whereas, an actuator with a poorly-tuned PID controller may reduce the performance of the closed-loop system dramatically or may even cause instability of the closed-loop system.

In the present work, we design a monitoring system to detect poorly-tuned PID controller used to regulate control actuators. The monitoring system employs a model-based dynamic  filter used to compute suitable residuals that are influenced by PID controller behavior and threshold-levels that are computed from historical  closed-loop process data under normal operation to detect and isolate poorly-tuned low-level PID controllers. The  monitoring scheme   captures the difference between the response of the outputs of the closed-loop process as  obtained by the measurement sensors and the expected behavior under well-tuned PID controllers in order to detect poor PID tunings in the actuators. Some criteria are also developed to differentiate a poor PID tuning from an actuator fault based on the evolution of the magnitude and rate of change of the  with respect to time.  An automatic strategy for retuning the poorly-tuned PID controllers is also discussed. An example of a  nonlinear reactor-separator process network under an MPC controller with its actuators under low-level PID controllers is used to demonstrate the approach.