Abstract
In this paper, the authors propose a novel adaptive multilayer T-S fuzzy controller (AMTFC) with an optimized soft computing algorithm for a class of robust control uncertain nonlinear SISO systems. First, a new multilayer T-S fuzzy was created by combined multiple simple T-S fuzzy models with a sum function in the output. The multi-layer fuzzy model used in nonlinear identification has many advantages over conventional fuzzy models, but it cannot be created by the writer's experience or the trial and error method. It can only be created using an optimization algorithm. Then the parameters of the multilayer fuzzy model are optimized by the differential evolution DE algorithm is used to offline identify the nonlinear inverse system with uncertain parameters. The trained model was validated by a different dataset from the training dataset to guarantee the convergence of the training algorithm. Second, for robustly and adaptive purposes, the authors have proposed an additional adaptive fuzzy model based on Lyapunov stability theory combined with the optimized multilayer fuzzy. The adaptive fuzzy based on the sliding mode surface is designed to guarantee that the closed-loop system is asymptotically stable has been proved base on a Lyapunov stability theory. Furthermore, simulation tests are performed in the Matlab/Simulink environment that controlling a water level of a coupled tank with uncertain parameters are given to illustrate the effectiveness of the proposed control scheme. The proposed control algorithm is implemented in simulation with many different control parameters, and it is also compared with the conventional adaptive control algorithm and inverse controller. The simulation results also show the superior of the proposed controller than an adaptive fuzzy control or inverse controller when using the least mean square error standard.
Highlights
Fuzzy logic was first proposed in 1965 by Zadeh 1
The multi-layer fuzzy model used in nonlinear identification has many advantages over conventional fuzzy models, but it cannot be created by the writer's experience or the trial and error method
Paper 17 presented the problem of fuzzy control for nonlinear networked control systems with packet dropouts and parameter uncertainties based on the interval type-2 fuzzy-model-based approach
Summary
Fuzzy logic was first proposed in 1965 by Zadeh 1. The advantage of T–S fuzzy systems is that they allow us to use a set of local linear systems with corresponding membership functions to represent nonlinear systems. Based on the T-S fuzzy model of a plant, papers 10–13 introduced a fuzzy control design method for nonlinear systems with a guaranteed H2/∞ model reference tracking performance. Type-2 fuzzy sets 14–16 have been shown that they prove better than type-1 ones both on representing the nonlinear systems and handling the uncertainties. Paper 17 presented the problem of fuzzy control for nonlinear networked control systems with packet dropouts and parameter uncertainties based on the interval type-2 fuzzy-model-based approach. Paper 18 introduced an inverse controller based on a type-2 fuzzy model control. Unlike a traditional Fuzzy set, multilayer Fuzzy model can’t be built
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Science & Technology Development Journal - Engineering and Technology
Disclaimer: 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.