Abstract
A proposed architecture to design the optimal parameters of Membership Functions (MFs) of Type-1 Fuzzy Logic Systems (T1FLSs) using the Chicken Search Optimization (CSO) is applied to three Fuzzy Logic Controllers (FLCs) in this paper. Two types of MFs are considered in the study: triangular and trapezoidal ones. The performance and efficiency of the CSO algorithm are particularly good when perturbations are added during the execution in each control problem. Two benchmark control problems: Water Tank Controller and Inverted Pendulum Controller are considered for testing the proposed approach. Also, the optimal design of a fuzzy controller for trajectory tracking of an Autonomous Mobile Robot (AMR) is considered to test the CSO. The main goal is to highlight the efficiency of CSO algorithm in finding optimal fuzzy controllers of non-linear plants. Two types of perturbations are considered in each control problem. Results show that the CSO algorithm presents excellent results in the field of Fuzzy Logic Controllers. Two types of Fuzzy Inference Systems: Takagi-Sugeno and Mamdani FLSs, are implemented in this paper. The most important metrics usually applied in control are used in this paper, such as: Integral Time Absolute Error (ITAE), Integral Time Squared Error (ITSE), Integral Absolute Error (IAE), Integral Square Error (ISE), Mean Square Error (MSE), and Root Mean Square Error (RMSE).
Highlights
Many works of the Chicken Search Optimization (CSO) algorithm have been presented in control problems, but in this paper, the more important contribution is to find the optimal design of the Membership Functions (MFs) of a Type-1 Fuzzy Logic System (T1FLS) applied to control problems
In the case of the third control problem the best average in Root Mean Square Error (RMSE) is with the noise of band-limited perturbation
The control problem of controlling the trajectory in an autonomous mobile robot shows a similar behavior even when perturbation is added in the model (Figure 24), whereby, this result allows evaluation of the efficiency and excellent performance of the CSO algorithms in the stabilization of fuzzy controllers
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Some works include [18], where a comparative study of bio-inspired algorithms applied in the optimization of fuzzy systems is presented by Miramontes et al, and [19], where a prediction of cervical cancer using Chicken Swarm Optimization is presented by Tripathi et al. Control is a study area of much interest for several authors; for example, in [20]. 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. Many works of the CSO algorithm have been presented in control problems, but in this paper, the more important contribution is to find the optimal design of the Membership Functions (MFs) of a Type-1 Fuzzy Logic System (T1FLS) applied to control problems.
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