Computational optimization analysis of feedforward plus feedback control scheme for boiler system

Abstract

Computational optimization via artificial intelligence has been considered as one of the key tools to gain competitiveness in Industrial Revolution 4.0. This paper proposes computational optimization analysis for designing the widely used industrial control systems - feedforward and feedback control schemes. Although several different optimal tunings for servo and regulatory control problems exist, their applications often present some challenges to plant operators. Plant operators often face difficulties to obtain satisfactory PID controller settings by using the conventional tuning methods, which rely heavily on engineering experience and skills. In the proposed intelligent tuning method for the feedforward plus feedback control system, the closed-loop stability region was first established, which then shall provide the upper and lower limits for computational optimization analysis via Genetic Algorithm. Based on a jacketed reactor case study, the performance of feedforward plus feedback control scheme tuned via Genetic Algorithm was compared to that tuned via Ziegler-Nichols tuning. Comparison of performances showed that computational optimization method via Genetic Algorithm gave improved performances in terms of servo and regulatory control objectives.