Self-tuning Nonlinear PID Controller for FOPTD Processes

Proportional-integral-derivative (PID) control has been extensively utilized in industrial production due to its inherent advantages; however, the management of the time-varying characteristics and nonlinearities present in industrial processes using standard fixed-parameter PID control is challenging, often resulting in difficulties for advanced technology applications. To address these issues, this study introduces a self-tuning controller for first-order plus time delay (FOPTD) processes, which incorporates both a nonlinear PID (NPID) control function, tuning rule, and a parameter estimation function. Specifically, the NPID control function is realized using a nonlinear PID controller whose optimal parameters are continuously updated by a precalculated tuning rule, while the self-tuning function is designed to identify an FOPTD model for the process by using a short period of operational behavior and a genetic algorithm, subsequently tuning the NPID parameters based on the identified model parameters. The efficacy and advantage of this proposed method are validated based on a comprehensive set of simulations performed on three distinct processes.