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Population-Based vs. Single Point Search Meta-Heuristics for a PID Controller Tuning

Population-Based vs. Single Point Search Meta-Heuristics for a PID Controller Tuning

Olympia Roeva, Tsonyo Slavov, Stefka Fidanova
ISBN13: 9781466644502|ISBN10: 1466644508|EISBN13: 9781466644519
DOI: 10.4018/978-1-4666-4450-2.ch007
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MLA

Roeva, Olympia, et al. "Population-Based vs. Single Point Search Meta-Heuristics for a PID Controller Tuning." Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications, edited by Pandian M. Vasant, IGI Global, 2014, pp. 200-233. https://doi.org/10.4018/978-1-4666-4450-2.ch007

APA

Roeva, O., Slavov, T., & Fidanova, S. (2014). Population-Based vs. Single Point Search Meta-Heuristics for a PID Controller Tuning. In P. Vasant (Ed.), Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications (pp. 200-233). IGI Global. https://doi.org/10.4018/978-1-4666-4450-2.ch007

Chicago

Roeva, Olympia, Tsonyo Slavov, and Stefka Fidanova. "Population-Based vs. Single Point Search Meta-Heuristics for a PID Controller Tuning." In Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications, edited by Pandian M. Vasant, 200-233. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-4450-2.ch007

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Abstract

This chapter presents a comparison of population-based (e.g. Genetic Algorithms (GA), Firefly Algorithm (FA), and Ant Colony Optimization (ACO))and single point search meta-heuristic methods (e.g. Simulated Annealing (SA), Threshold Accepting (TA), and Tabu Search (TS)) applied to an optimal tuning of a universal digital proportional-integral-derivative (PID) controller. The PID controllers control feed rate and maintain glucose concentration at the desired set point for an E. coli MC4110 fed-batch cultivation process. The model of the cultivation process is represented by dynamic non-linear mass balance equations for biomass and substrate. In the control the design measurement, process noise, and time delay of the glucose measurement system were taken into account. To achieve good closed-loop system the constants (Kp, Ti, Td, b, c and N) were tuned in the PID controller algorithm so the controller can provide control action designed for the specific process requirements resulting in an optimal set of PID controller settings. For a time the controllers set and maintained the control variable at the desired set point during the E. coli MC4110 fed-batch cultivation process. Average, best, and worst objective function values and PID controller's parameters were used as criteria to compare the performance of the considered meta-heuristic algorithms. This indicates that the population-based meta-heuristics performs better than the single point search methods. GA and ACO show better performance than FA. It also indicates that TS results are comparable to those of FA. The results show that SA and TA algorithms failed to solve the PID controller tuning problem.

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