scholarly journals Grey FNN control and robustness design for practical nonlinear systems

2021 ◽  
Author(s):  
ZY Chen ◽  
◽  
Yahui Meng ◽  
Ruei-yuan Wang ◽  
Timothy Chen ◽  
...  
Keyword(s):  
Differential Equations ◽  
Fuzzy Neural Network ◽  
Ride Comfort ◽  
Controller Design ◽  
Bat Algorithm ◽  
Control Law ◽  
Mathematical Framework ◽  
Controlled Systems ◽  
Active Model ◽  
Fuzzy Neural

To ensure asymptomatic stability and improve vehicle ride comfort, this paper develops a fuzzy neural network (FNN) based on the evolved bat algorithm (EBA) to design adaptive backstepping controllers with gray signal predicators. A recoil method is used to evaluate the nonlinearity of the controlled systems and to derive the control law which is evolved for the tracking of the signals. A group of grey differential equations are applied for the grey model (GM) (n, h), which is an active model where h is the number of considered variables and n is the order of the grey differential equations. In the article, the Discrete GM (2.1) is used to obtain the advanced motion of the nonlinear system, so that the command controller can prove the Lyapunov stability and feasibility of the entire scheme through the Lyapunov-like lemma. The controller design criteria are demonstrated for mechanical elastic wheels (MEW) to establish a viable mathematical framework for the new wheels.

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.

Adaptive asymmetric fuzzy neural network controller design via network structuring adaptation

2008 ◽  
Vol 159 (20) ◽  
pp. 2627-2649 ◽  
Author(s):  
Chun-Fei Hsu ◽  
Ping-Zong Lin ◽  
Tsu-Tian Lee ◽  
Chi-Hsu Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Controller Design ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural

scholarly journals Grey Signal Predictor and Fuzzy Controls for Active Vehicle Suspension Systems via Lyapunov Theory

2021 ◽  
Vol 16 (3) ◽  
Author(s):  
Tim Chen ◽  
Chih Ching Hung ◽  
Yu Ching Huang ◽  
John C.Y. Chen ◽  
Samiur Rahman ◽  
...  
Keyword(s):  
Bat Algorithm ◽  
Adaptive Controller ◽  
Lyapunov Theory ◽  
Vehicle Suspension ◽  
Control Law ◽  
Backstepping Method ◽  
Application Range ◽  
Suspension Systems ◽  
Fuzzy Neural ◽  
The Stability

In order to investigate and decide that the vehicle asymptotic vibration stability and improved comfort, the present paper deals with a fuzzy neural network (NN) evolved bat algorithm (EBA) backstepping adaptive controller based on grey signal predictors. The Lyapunov theory and backstepping method is utilized to appraise the math nonlinearity in the active vehicle suspension as well as acquire the final simulation control law in order to track the suitable signal. The Discrete Grey Model DGM (2,1) have been thus used to acquire prospect movement of the suspension system, so that the command controller can prove the convergence and the stability of the entire formula through the Lyapunov-like lemma. The controller overspreads the application range of mechanical elastic vehicle wheel (MEVW) as well as lays a favorable theoretic foundation in adapting to new wheels.

Application of Semi-Active Inerter in Semi-Active Suspensions Via Force Tracking

2016 ◽  
Vol 138 (4) ◽  
Author(s):  
Michael Z. Q. Chen ◽  
Yinlong Hu ◽  
Chanying Li ◽  
Guanrong Chen
Keyword(s):  
Active Control ◽  
Ride Comfort ◽  
Controller Design ◽  
Active Suspension ◽  
Damping Coefficient ◽  
Control Law ◽  
Control Force ◽  
Car Model ◽  
Force Tracking ◽  
Active Damper

This paper investigates the application of semi-active inerter in semi-active suspension. A semi-active inerter is defined as an inerter whose inertance can be adjusted within a finite bandwidth by online control actions. A force-tracking approach to designing semi-active suspension with a semi-active inerter and a semi-active damper is proposed in this paper. Two parts are required in the force-tracking strategy: a target active control law and a proper algorithm to adjust the inertance and the damping coefficient online to track the target active control law. The target active control law is derived based on the state-derivative feedback control methodology in the “reciprocal state-space” (RSS) framework, which has the advantage that it is straightforward to use the acceleration information in the controller design. The algorithm to adjust the inertance and the damping coefficient is to saturate the active control force between the maximal and the minimal achievable suspension forces of the semi-active suspension. Both a quarter-car model and a full-car model are considered in this paper. Simulation results demonstrate that the semi-active suspension with a semi-active inerter and a semi-active damper can track the target active control force much better than the conventional semi-active suspension (which only contains a semi-active damper) does. As a consequence, the overall performance in ride comfort, suspension deflection, and road holding is improved, which effectively demonstrates the necessity and the benefit of introducing semi-active inerter in vehicle suspension.

A Novel Robust Pid Controller Design By Fuzzy Neural Network

2008 ◽  
Vol 4 (4) ◽  
pp. 433-438 ◽  
Author(s):  
Ching-Hung Lee ◽  
Yi-Hsiung Lee ◽  
Ching-Cheng Teng
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Controller Design ◽  
Fuzzy Neural ◽  
Pid Controller Design
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