Controller performance assessment in set point tracking and regulatory control

Recent critiques of minimum variance benchmarking for single-input-single-output control loops have focused on the need for assessment of performance during set point changes and also on the need to pay attention to the movements in the manipulated variable. This paper examines factors that influence the minimum variance performance measure of a SISO control loop. It discusses the reasons why performance during set point changes differs from the regulatory performance during operation at a constant set point. The results demonstrate how regulatory performance is influenced by the nature of a disturbance, and that correlation of signals within a control loop can indicate whether the disturbance is random or deterministic. The paper is illustrated with simulated, experimental and industrial examples

A self-validating control system based approach to plant fault detection and diagnosis

An approach is proposed in which fault detection and diagnosis (FDD) tasks are distributed to separate FDD modules associated with each control system located throughout a plant. Intended specifically for those control systems that inherently eliminate steady state error, it is modular, steady state based, requires very little process specific information and therefore should be attractive to control systems implementers who seek economies of scale. The approach is applicable to virtually all types of process plant, whether they are open loop stable or not, have a type or class number of zero or not and so on. Based on qualitative reasoning, the approach is founded on the application of control systems theory to single and cascade control systems with integral action. This results in the derivation of cause-effect knowledge and fault isolation procedures that take into account factors like interactions between control systems, and the availability of non-control-loop-based sensors