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Multiobjective optimization of 2DOF controller using Evolutionary and Swarm intelligence enhanced with TOPSIS.

Haresh A Suthar1, Jagrut J Gadit2

  • 1Electronics & Communication Engineering Department of Parul Institute of Technology, Parul University, Po. Limda, Vadodara, Gujarat, India.

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Summary

This study optimizes Two Degree Of Freedom (2DOF) controller parameters for shell and tube heat exchangers using Evolutionary (NSGA-II, NSGA-III) and Swarm Intelligence (MOPSO) algorithms with TOPSIS. The methods effectively balance setpoint tracking and disturbance rejection for improved control performance.

Keywords:
Electrical engineering

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Area of Science:

  • Process Control
  • Optimization Algorithms
  • Heat Exchanger Dynamics

Background:

  • Optimizing Two Degree Of Freedom (2DOF) controllers is crucial for enhancing shell and tube heat exchanger performance.
  • Conflicting objectives in setpoint tracking and disturbance rejection (flow and temperature variations) present a significant control challenge.
  • Existing optimization methods may not adequately address the multi-objective nature of 2DOF controller tuning.

Purpose of the Study:

  • To employ Evolutionary (NSGA-II, NSGA-III) and Swarm Intelligence (MOPSO) algorithms, enhanced by TOPSIS, for optimizing 2DOF controller parameters.
  • To address the multi-objective optimization problem involving setpoint tracking and dual disturbance rejections in a shell and tube heat exchanger.
  • To comparatively analyze the performance of different optimization algorithms in achieving robust controller tuning.

Main Methods:

  • Utilized Non-dominated Sorting Genetic Algorithm II (NSGA-II) and NSGA-III, alongside Multi-Objective Particle Swarm Optimization (MOPSO).
  • Integrated the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for multi-criteria decision-making and solution ranking.
  • Evaluated controller performance using Integral Absolute Error (IAE), Integral Squared Error (ISE), and Integral Time Absolute Error (ITAE) criteria.

Main Results:

  • Generated Pareto optimal solution sets for the five 2DOF controller parameters under different objective functions.
  • TOPSIS effectively reduced the Pareto sets to single optimal solutions for each algorithm.
  • Comparative analysis showed NSGA-II, NSGA-III, and MOPSO yielded distinct sets of optimal parameters, with performance varying across IAE, ISE, and ITAE criteria.

Conclusions:

  • The combined approach of evolutionary/swarm algorithms with TOPSIS provides a robust framework for optimizing 2DOF controllers in heat exchangers.
  • Different algorithms offer trade-offs in performance metrics such as overshoot, tracking error, disturbance rejection, and settling time.
  • The study demonstrates the effectiveness of advanced optimization techniques in achieving superior control performance for complex industrial processes.