PID Controller Tuning based on Metaheuristic Algorithms for Bioprocess Control

ABSTRACT

This paper presents an optimal tuning of a universal digital PID controller using metaheuristics as Genetic Algorithms (GA), Simulated Annealing (SA) and Tabu Search (TS). The controllers were used to control the feed rate and to maintain the glucose concentration at the desired set point for an E. coli MC4110 fed-batch cultivation process. The mathematical model of the cultivation process was represented by the dynamic mass balance equations for biomass and substrate. In the control algorithm the design measurement and process noise as well as the time delay of the glucose measurement system were taken into account. To achieve good closed-loop system performance metaheuristics based controller tuning was done. By tuning the constants (K_p T_i, T_d b, c and N) in the PID controller algorithm, the controller can provide control action designed for the specific process requirements. To evaluate the significance of the tuning procedure and controller performance different criteria were used. Objective function values and CPU time were used as criteria to compare the performance of the three metaheuristic algorithms—GA, SA and TS. A series of procedures for PID controller tuning were performed using competing techniques and criteria. As a result the set of optimal PID controller settings was obtained. For a short time the controller set the control variable and maintained it at the desired set point during the E. coli MC4110 fed-batch cultivation process. The simulation results indicate that the proposed metaheuristic algorithms are effective and efficient, and demonstrate that the applied techniques exhibit a significant performance improvement over classical optimization methods.

Keywords:

• E. coli
• genetic algorithm
• simulated annealing
• tabu search
• PID controller
• tuning
• bioprocesses