Affiliation:
1. Department of Electrical Engineering, University of 20 Août 1955 Skikda, B.P.26, El-Hadaiek Skikda, Algeria
Abstract
Here, we develop a fuzzy controller using a new online self-adapting design. The objective of this work is to control a nonlinear process by using a one-dimensional input rule variable, instead of error and error variation. The initial limits of the fuzzy logic membership functions are mostly depend on experiments and previous knowledge of the dynamic process behaviors. Generally, the membership function parameters have a significant impact on control signal amplitude and, consequently on the convergence and stability of the controller-plant system. The proposed technique determines the limits of the antecedent membership functions online using the kth and k - 1th outputs of the controlled plant and reference model, respectively. Meanwhile, the limits of the consequent membership functions are calculated using error and error variation. This approach ensures: (i) that the input/output variables have the required fuzzy space, (ii) the controlled plant follows the desired reference model, and (iii) the control signal amplitude is within acceptable limits. Additionally, (iiii) it takes into account the dynamic variability of the process and the existence of an overshoot. The membership function parameters are updated continuously through a self-adapting procedure, ensuring improved control performance. Ultimately, the proposed approach is improved using two nonlinear systems.
Subject
Artificial Intelligence,General Engineering,Statistics and Probability
Reference27 articles.
1. Full-State Regulation Control of Asymmetric Underactuated Surface Vehicles;Wang;IEEE Trans. Ind. Electron.,2019
2. Zhao F. , Yao H. , Chen X. , Cao J. and Qiu J. , Robust H∞Sliding Mode Control for a Class of Singular Stochastic NonlinearSystems: Robust H∞ Sliding Mode Control for SingularStochastic System, Asian J. Control 21 (2018).
3. Adaptive Fuzzy Control Design for Stochastic Nonlinear Switched Systems With Arbitrary Switchings and Unmodeled Dynamics;Li;IEEE Trans. Cybern.,2016
4. Amador Angulo G. and Castillo O. , A new fuzzy bee colony optimization with dynamic adaptation of parameters using interval type-2 fuzzy logic for tuning fuzzy controllers, Soft Comput. 22 (2018).
5. Optimization of fuzzy controller design using a new bee colony algorithm with fuzzy dynamic parameter adaptation;Camilo;Appl. Soft Comput.,2016