Fuzzy Logic Controller Parameter Optimization Using Metaheuristic Cuckoo Search Algorithm for a Magnetic Levitation System

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.

An Improved Multicriteria Optimization Method for Solving the Electric Vehicles Planning Issue in Smart Grids via Green Energy Sources

“…Additionally, the flock search strategy is a form of mathematical population diversity. This strategy is based on the Uncontrolled Classifying Genetic Algorithm (UCGA), which is a multicriteria optimization algorithm suggested by Srinivas and Deb [22]. The favorable performance of this method in discovering a multicriteria optimal front and preserving the variety of the population is confirmed.…”

“…Arithmetically, the 3 rules can be investigated, in turn, as components of crossover, elitism, and mutation. These three components work together, by which the effectiveness of the algorithm can be evaluated [22]. Nevertheless, the Multi-Criteria Cuckoo Search (MCCS) is not without its deficiencies.…”

An Efficient Chicken Search Optimization Algorithm for the Optimal Design of Fuzzy Controllers

“…A wide variety of algorithms have been developed in the field of control. Some related works are mentioned as follows: two applications of Bee Colony Optimization (BCO) are presented in [31,32], a Salp Swarm Algorithm (SWA) is studied in [33], a Bio-inspired Chaotic Fruit Fly Algorithm (FFA) is presented in [34], a Particle Swarm Optimization (PSO) is presented in [35], a Flower Pollination Algorithm (FPA) is presented in [36], a Cuckoo Search Algorithm (CSA) is presented in [37], a Grey Wolf Algorithm (GWA) is presented in [38], a Bat Algorithm (BA) is presented in [39], a Black Hole Algorithm (BHA) is presented in [40], a Firefly Algorithm (FA) is presented in [41], some bio-inspired algorithms are analyzed and presented in [42], a Cat Swarm Optimization (CSO) Algorithm is presented in [43], and an Ant Colony Optimization (ACO) algorithm is presented in [44]. For this reason, in this paper some benchmark control problems are studied and analyzed with Chicken Search Optimization Algorithm (CSO) for solving problems and demonstrated the efficiency in this algorithm in control problems.…”

Design and Real-Time Implementation of Takagi–Sugeno Fuzzy Controller for Magnetic Levitation Ball System

“…In [8], four novel controllers called fractional static sliding mode controller, fractional integral static sliding mode controller, fractional dynamic sliding mode controller and fractional integral dynamic sliding mode controller are proposed and and is applied in a magnetic levitation system. In [9], an optimization procedure based on the CS(cuckoo search) algorithm is presented to optimize all the parameters of the fuzzy logic control which involve not only the membership functions positions but also the rule base and it is applied to a nonlinear magnetic levitation system. In [10], a sliding mode controller (SMC) is designed to regulate the current, which in turn controls the position through an electromagnet.…”