Advanced Metaheuristics-Based Approach for Fuzzy Control Systems Tuning

  • Soufiene Bouallègue
  • Fatma ToumiEmail author
  • Joseph Haggège
  • Patrick Siarry
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 319)


In this study, a new advanced metaheuristics-based optimization approach is proposed and successfully applied to design and tuning of a PID-type Fuzzy Logic Controller (FLC). The scaling factors tuning problem of the FLC structure is formulated and systematically resolved, using various constrained metaheuristics such as the Differential Search Algorithm (DSA), Gravitational Search Algorithm (GSA), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). In order to specify more time-domain performance control objectives of the proposed metaheuristics-tuned PID-type FLC, different optimization criteria such as Integral of Square Error (ISE) and Maximum Overshoot (MO) are considered and compared The classical Genetic Algorithm Optimization (GAO) method is also used as a reference tool to measure the statistical performances of the proposed methods. All these algorithms are implemented and analyzed in order to show the superiority and the effectiveness of the proposed fuzzy control tuning approach. Simulation and real-time experimental results, for an electrical DC drive benchmark, show the advantages of the proposed metaheuristics-tuned PID-type fuzzy control structure in terms of performance and robustness.


Particle Swarm Optimization Particle Swarm Optimization Algorithm Fuzzy Controller Fuzzy Logic Controller Gravitational Search Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Soufiene Bouallègue
    • 1
  • Fatma Toumi
    • 1
    • 2
    Email author
  • Joseph Haggège
    • 1
  • Patrick Siarry
    • 2
  1. 1.Research Laboratory in Automatic Control LA.R.ANational Engineering School of Tunis (ENIT)TunisTunisia
  2. 2.Signals, Images and Intelligent Systems Laboratory, LiSSi-EA-3956University Paris-Est Créteil Val de MarneCréteilFrance

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