Fuzzy systems design by clustering-aided ant colony optimization for plant control

Abstract

This paper proposes fuzzy systems design by clustering-aided ant colony optimization (ACO) algorithm (CACO). The objective of CACO is to improve both the design efficiency of fuzzy systems and its performance. In CACO, structure of a fuzzy system, including the number of rules and fuzzy sets in each input variable, is created on-line by a newly proposed fuzzy clustering. In contrast to conventional grid-type partition, the antecedent part of a fuzzy system is flexibly partitioned, and the phenomenon of highly overlapped fuzzy sets is avoided. Once a new rule is generated, the consequence is selected from a list of candidate control actions by ACO. In ACO, the route of an ant is regarded as a combination of consequent actions selected from every rule. A pheromone matrix among all candidate actions is constructed and an on-line learning algorithm for heuristic value update is proposed. Searching for the best one among all consequence combinations involves using the pheromone matrix and heuristic values. To verify the performance of CACO on fuzzy systems design, simulations on nonlinear system control, water bath temperature control and chaotic system control are performed. Simulations on these problems and comparisons with other algorithms have demonstrated the performance of CACO

Process control using genetic algorithm and ant colony optimization algorithm

“…Determining the optimal values of these numbers is another topic to be studied. Although there is no theoretical basis for selecting these parameters, a few systematic approaches are reported, one of which is heuristic [30]. Figure 3 shows the realized one hidden layer MLP network.…”

A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control

“…These studies can be divided into three groups as genetic algorithm (Herrera, Lozano, & Verdegay, 1998;Leng, McGinnity, & Prasad, 2006;Seng, Khalid, & Yusof, 1999), PSO (Aliyari, Teshnehlab, & Sedigh, 2007;Chatterjee & Watanabe, 2006;Daniel & Xiaodong, 2006;Eberhart & Kennedy, 1995;Mukherjee & Ghoshal, 2007;Pulasinghe, Chatterjee, & Watanabe, 2005;Wong, Wang, & Li, 2008) and ant algorithm (Juang & Lo, 2007;Juang & Lo, 2008). Wong et al (2008) proposed a motion control structure with a distance fuzzy controller and an angle fuzzy controller for the twowheeled mobile robot.…”

Design and application of Type-2 fuzzy logic system based on improved ant colony algorithm

“…Yazdi (2016) combined GA with FLS, and it was applied to the optimization of off center braced frame system, compared with the structure optimized only by GA, the effect was more remarkable. Juang and Lo, 2007; Juang 2014 combined ant colony optimization with fuzzy cluster to design FLS, and used it for nonlinear plant tracking control; compared with PSO and GA, the results were more precise. In addition, they also adopted cooperative continuous ant colony optimization to design FLS, and used it for water bath temperature control system; compared with QPSO and GA, the results were more precise in 2014.…”