Abstract
The main benefits of fuzzy logic control (FLC) allow a qualitative knowledge of the desired system’s behavior to be included as IF-THEN linguistic rules for the control of dynamical systems where either an analytic model is not available or is too complex due, for instance, to the presence of nonlinear terms. The computational structure requires the definition of the FLC parameters namely, membership functions (MF) and a rule base (RB) defining the desired control policy. However, the optimization of the FLC parameters is generally carried out by means of a trial and error procedure or, more recently by using metaheuristic nature-inspired algorithms, for instance, particle swarm optimization, genetic algorithms, ant colony optimization, cuckoo search, etc. In this regard, the cuckoo search (CS) algorithm as one of the most promising and relatively recent developed nature-inspired algorithms, has been used to optimize FLC parameters in a limited variety of applications to determine the optimum FLC parameters of only the MF but not to the RB, as an extensive search in the literature has shown. In this paper, an optimization procedure based on the CS algorithm is presented to optimize all the parameters of the FLC, including the RB, and it is applied to a nonlinear magnetic levitation system. Comparative simulation results are provided to validate the features improvement of such an approach which can be extended to other FLC based control systems.
Highlights
Most problems in the real world are often very challenging to solve, and many applications have to deal with non-deterministic polynomial-time hard (i.e., NP-hard) problems
The objective of this paper is to propose a methodology to optimize fuzzy logic control (FLC) parameters which involve the membership functions (MF)’s positions and the rule base (RB), which is the novelty of the presented approach, using for this purpose the cuckoo search (CS) algorithm
The CS algorithm used magnetic levitation system (MLS) modeling to simulate the real performance of the system with an 8 mm height set point and tuned the FLC according to MLS desired performance parameters
Summary
Most problems in the real world are often very challenging to solve, and many applications have to deal with non-deterministic polynomial-time hard (i.e., NP-hard) problems. Meta-heuristic nature-inspired algorithms have been given special attention due to their great capability in solving optimization problems in a wide range of applications such as operation and control of electric power systems [8,9,10,11,12], chemical processes [13,14,15], job scheduling [16,17,18], vehicle routing [19,20], autonomous vehicles control [21], mobile networking [22,23], multi-objective optimization [24,25,26], image processing [27], etc.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
