Advanced Metaheuristics-Based Approach for Fuzzy Control Systems Tuning

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.

[1]  Chuen-Chien Lee,et al.  Fuzzy logic in control systems: fuzzy logic controller. II , 1990, IEEE Trans. Syst. Man Cybern..

[2]  Stephen Yurkovich,et al.  Fuzzy Control , 1997 .

[3]  Engin Yesil,et al.  Self-tuning of PID-type fuzzy logic controller coefficients via relative rate observer , 2003 .

[4]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[5]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[6]  Shankar Chakraborty,et al.  Differential search algorithm-based parametric optimization of electrochemical micromachining processes , 2014 .

[7]  Zbigniew Michalewicz,et al.  Advances in Metaheuristics for Hard Optimization , 2008, Advances in Metaheuristics for Hard Optimization.

[8]  R. Venkata Rao,et al.  Mechanical Design Optimization Using Advanced Optimization Techniques , 2012 .

[9]  Patrick Siarry,et al.  Particle Swarm Optimization-based design of polynomial RST controllers , 2013, 10th International Multi-Conferences on Systems, Signals & Devices 2013 (SSD13).

[10]  Mounir Ayadi,et al.  PID-type fuzzy logic controller tuning based on particle swarm optimization , 2012, Eng. Appl. Artif. Intell..

[11]  Stefan Preitl,et al.  Gravitational Search Algorithms in Fuzzy Control Systems Tuning , 2011 .

[12]  P. Siarry,et al.  Non-dominated Sorting Gravitational Search Algorithm , 2011 .

[13]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[14]  Constantin V. Negoita,et al.  On Fuzzy Systems , 1978 .

[15]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[16]  Mohamed Benrejeb,et al.  A New Method for Tuning PID-Type Fuzzy Controllers Using Particle Swarm Optimization , 2012 .

[17]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[18]  Hung-Yuan Chung,et al.  A PID type fuzzy controller with self-tuning scaling factors , 2000, Fuzzy Sets Syst..

[19]  Ahmad Taher Azar,et al.  Overview of Type-2 Fuzzy Logic Systems , 2012, Int. J. Fuzzy Syst. Appl..

[20]  Chuen-Chien Lee.  FUZZY LOGIC CONTROL SYSTEMS: FUZZY LOGIC CONTROLLER - PART I , 1990 .

[21]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[22]  Johann Dréo,et al.  Metaheuristics for Hard Optimization: Methods and Case Studies , 2005 .

[23]  Pinar Civicioglu,et al.  Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm , 2012, Comput. Geosci..

[24]  Mounir Ayadi,et al.  Design of Fuzzy Flatness-Based Controller for a DC Drive , 2010, Control. Intell. Syst..

[25]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[26]  Mohamed Benrejeb,et al.  Particle swarm optimization-based fixed-structure ℋ∞ control design , 2011 .

[27]  Rajiv Tiwari,et al.  Optimization of needle roller bearing design using novel hybrid methods , 2014 .

[28]  Masaharu Mizumoto,et al.  PID type fuzzy controller and parameters adaptive method , 1996, Fuzzy Sets Syst..

[29]  Dervis Karaboga,et al.  A comprehensive survey: artificial bee colony (ABC) algorithm and applications , 2012, Artificial Intelligence Review.

[30]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[31]  Ahmad Taher,et al.  Adaptive Neuro-Fuzzy Systems , 2010 .

[32]  Ahmad Taher Azar,et al.  Robust IMC–PID tuning for cascade control systems with gain and phase margin specifications , 2014, Neural Computing and Applications.

[33]  Patrick Siarry,et al.  A survey on optimization metaheuristics , 2013, Inf. Sci..

[34]  İlyas Eker,et al.  Fuzzy logic control to be conventional method , 2006 .

[35]  Stefan Preitl,et al.  Gravitational search algorithm-based design of fuzzy control systems with a reduced parametric sensitivity , 2013, Inf. Sci..