Controller Auto-Tuning Based on Control Performance Monitoring

Abstract

In practice, it is the norm to perform controller tuning only at the commissioning stage and never again. A control loop that worked well at one time is prone to degradation over time unless regular maintenance is undertaken. Typically, 30 % of industrial loops have poor tuning, and 85 % of loops have sub-optimal tuning. There are many reasons for the degradation of control loop performance, including changes in disturbance characteristics, interaction with other loops, changes in production characteristics (e.g. plant throughput, product grade), etc. Also, many loops are still “tuned by feel” without considering appropriate tuning methods—a practice often leading to very strange controller behaviour. This chapter presents new tuning methods that treat controller tuning in the context of control performance monitoring and thus substantially extend the traditional field of controller auto-tuning. This means that control performance measures are continuously monitored on a regular basis, i.e. during normal operation, and performance statistics used to schedule loop retuning and automatically determine the optimal controller parameters. It starts with recalling the basic concepts of PID auto-tuning and adaptation as well as a classification of CPM-based controller re-tuning methods. Techniques that deliver optimal controller parameters by solving an optimisation problem are then described. Subsequently new re-tuning methods are presented, which simultaneously provide the assessment of the controller performance and finding the optimal controller settings in an iterative way on the closed loop. Simulation studies are presented to compare the different techniques and make suggestions for using them.