Closed-Loop PID Re-Tuning in a Digital Twin by Re-Playing Past Setpoint and Load Disturbance Data

摘要

The PID controller is the most widely used basic regulatory control algorithm.PID control is important in chemical engineering processes as it plays a critical role to form the basis of advanced process control and optimization systems such as model predictive control (MPC) and real-time optimization (RTO).However,its performance can vary greatly on the tuning of its three (3) parameters.There are several different types of PID tuning rules or heuristics reviewed in [1].Unfortunately,it can be demanding for operators,instrument technicians,process engineers,or inexperienced process control engineers to choose the most suitable rule and use the rule properly in the actual or physical operating system or environment.However,this demanding exercise is facilitated by excellent automated tuning selection within Distributed Control Systems (DCS),Programmable Logic Controllers (PLC),or through specialized tuning software.Tuning rules are valuable as a starting point for further manual tuning.Optimization-based PID tuning is the other option and,thanks to the advances in computational power,can be a viable approach provided the objective function and setpoint/load disturbance scenarios are properly managed.Numerical optimization techniques are divided into two different categories.One is a model gradient-based optimization and the other is non-gradient based or derivative-free optimization.However,because of the non-convexity nature of the PID tuning model,a majority of the PID tuning research has been done using non-gradient optimization algorithms such as particle swarm [2,3],and extremum seeking [4] algorithms.Another group of research study has been done using random search methods or meta-heuristics including genetic algorithm [5],for example.