Evolved Fuzzy NN Control for Discrete-Time Nonlinear Systems

2019 ◽  
Vol 29 (01) ◽  
pp. 2050015 ◽  
Author(s):  
Tim Chen ◽  
A. Babanin ◽  
Assim Muhammad ◽  
B. Chapron ◽  
C. Y. J. Chen
Keyword(s):  
Nonlinear Systems ◽  
Asymptotic Stability ◽  
Discrete Time ◽  
Fuzzy Neural Network ◽  
Bat Algorithm ◽  
Linear Matrix ◽  
Rule Base ◽  
Sufficiency Condition ◽  
Fuzzy Neural ◽  
Linear Differential

To guarantee the asymptotic stability of discrete-time nonlinear systems, this paper proposes an Evolved Bat Algorithm (EBA) fuzzy neural network (NN) controller. In the evolved fuzzy NN modeling, an NN model and linear differential inclusion (LDI) representation are established for arbitrary nonlinear dynamics. This representation is constructed by the use of sector nonlinearity to convert a nonlinear model to the multiple rule base of the linear model, and a new sufficiency condition to guarantee asymptotic stability in the Lyapunov function is implemented in terms of linear matrix inequalities. The proposed method is an enhancement of existing methods which produces good results.

Evolved auxiliary controller with applications to aerospace

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tim Chen ◽  
N. Kapronand ◽  
C.Y. Hsieh ◽  
J. Cy Chen
Keyword(s):  
Asymptotic Stability ◽  
High Speed ◽  
Bat Algorithm ◽  
Parabolic Partial Differential Equation ◽  
Linear Matrix ◽  
Rule Base ◽  
Aerospace Vehicles ◽  
Content Type ◽  
Fuzzy Neural ◽  
Linear Differential

Purpose To guarantee the asymptotic stability of discrete-time nonlinear systems, this paper aims to propose an evolved bat algorithm fuzzy neural network (NN) controller algorithm. Design/methodology/approach In evolved fuzzy NN modeling, the NN model and linear differential inclusion representation are established for the arbitrary nonlinear dynamics. The control problems of the Fisher equation and a temperature cooling fin for high-speed aerospace vehicles will be described and demonstrated. The signal auxiliary controlled system is represented for the nonlinear parabolic partial differential equation (PDE) systems and the criterion of stability is derived via the Lyapunov function in terms of linear matrix inequalities. Findings This representation is constructed by sector nonlinearity, which converts the nonlinear model to a multiple rule base for the linear model and a new sufficient condition to guarantee the asymptotic stability. Originality/value This study also injects high frequency as an auxiliary and the control performance to stabilize the nonlinear high-speed aerospace vehicle system.

Intelligent fuzzy algorithm for nonlinear discrete-time systems

2019 ◽  
Vol 42 (7) ◽  
pp. 1358-1374
Author(s):  
Tim Chen ◽  
CYJ Chen
Keyword(s):  
Large Scale ◽  
Optimal Solution ◽  
Bat Algorithm ◽  
Linear Matrix ◽  
Neural Network Modeling ◽  
Rule Base ◽  
Large Scale System ◽  
Model Based ◽  
Linear Differential ◽  
The Stability

This paper is concerned with the stability analysis and the synthesis of model-based fuzzy controllers for a nonlinear large-scale system. In evolved fuzzy NN (neural network) modeling, the NN model and LDI (linear differential inclusion) representation are established for the arbitrary nonlinear dynamics. The evolved bat algorithm (EBA) is first incorporated in the controlled algorithm of stability conditions, which could rapidly find the optimal solution and raise the control performance. This representation is constructed by taking advantage of sector nonlinearity that converts the nonlinear model to a multiple rule base linear model. A new sufficient condition guaranteeing asymptotic stability is implemented via the Lyapunov function in terms of linear matrix inequalities. Subsequently, based on this criterion and the decentralized control scheme, an evolved model-based fuzzy H infinity set is synthesized to stabilize the nonlinear large-scale system. Finally, a numerical example and simulation is given to illustrate the results.

scholarly journals Delay-Partitioning Approach to Stability of Linear Discrete-Time Systems with Interval-Like Time-Varying Delay

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Priyanka Kokil ◽  
V. Krishna Rao Kandanvli ◽  
Haranath Kar
Keyword(s):  
Asymptotic Stability ◽  
Discrete Time ◽  
Global Asymptotic Stability ◽  
Linear Matrix ◽  
Time Varying ◽  
Time Varying Delay ◽  
Discrete Time Systems ◽  
Time Systems ◽  
Delay Partitioning ◽  
Varying Delay

This paper is concerned with the problem of global asymptotic stability of linear discrete-time systems with interval-like time-varying delay in the state. By utilizing the concept of delay partitioning, a new linear-matrix-inequality-(LMI-) based criterion for the global asymptotic stability of such systems is proposed. The proposed criterion does not involve any free weighting matrices but depends on both the size of delay and partition size. The developed approach is extended to address the problem of global asymptotic stability of state-delayed discrete-time systems with norm-bounded uncertainties. The proposed results are compared with several existing results.

scholarly journals Stability Analysis for Discrete-Time Stochastic Fuzzy Neural Networks with Mixed Delays

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
YaJun Li ◽  
Quanxin Zhu
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Sufficient Conditions ◽  
Fuzzy Neural Networks ◽  
Linear Matrix ◽  
Mixed Delays ◽  
The Novel ◽  
Free Weight ◽  
Fuzzy Neural ◽  
The Stability

This paper is concerned with the stability problem of a class of discrete-time stochastic fuzzy neural networks with mixed delays. New Lyapunov-Krasovskii functions are proposed and free weight matrices are introduced. The novel sufficient conditions for the stability of discrete-time stochastic fuzzy neural networks with mixed delays are established in terms of linear matrix inequalities (LMIs). Finally, numerical examples are given to illustrate the effectiveness and benefits of the proposed method.

scholarly journals Robust Preview Control for a Class of Uncertain Discrete-Time Lipschitz Nonlinear Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xiao Yu ◽  
Fucheng Liao ◽  
Jiamei Deng
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Tracking Error ◽  
Reference Signal ◽  
Original System ◽  
Linear Matrix ◽  
Preview Control ◽  
Lipschitz Nonlinear Systems ◽  
Error System ◽  
Discrete Integrator

This paper considers the design of the robust preview controller for a class of uncertain discrete-time Lipschitz nonlinear systems. According to the preview control theory, an augmented error system including the tracking error and the known future information on the reference signal is constructed. To avoid static error, a discrete integrator is introduced. Using the linear matrix inequality (LMI) approach, a state feedback controller is developed to guarantee that the closed-loop system of the augmented error system is asymptotically stable with H∞ performance. Based on this, the robust preview tracking controller of the original system is obtained. Finally, two numerical examples are included to show the effectiveness of the proposed controller.

Pattern Recognition Controller Based on Fuzzy Neural Network

2014 ◽  
Vol 915-916 ◽  
pp. 1140-1143
Author(s):  
Liang Cheng
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Pattern Recognition ◽  
Fuzzy Neural Network ◽  
Feasible Solution ◽  
Fuzzy Theory ◽  
Network Design Problem ◽  
Linear Matrix ◽  
Matrix Inequalities ◽  
Fuzzy Neural

A class of fuzzy neural network design problem H controller. By TS fuzzy theory, a model of nonlinear complex systems. Then, based on Lyapunov-Krasovskii functional and LMI technique, gives the design an H controller. By using the Matlab LMI toolbox, we can get the corresponding feasible solution of linear matrix inequalities. Finally, a numerical simulation examples are given to prove the correctness of the H controller.

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