Almost disturbance decoupling control of mimo nonlinear system subject to feedback linearization and a feedforward neural network: Application to half-car active suspension system

2010 ◽  
Vol 11 (4) ◽  
pp. 581-592 ◽  
Author(s):  
T. H. S. Li ◽  
C. J. Huang ◽  
C. C. Chen
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Feedback Linearization ◽  
Suspension System ◽  
Feedforward Neural Network ◽  
Decoupling Control ◽  
Almost Disturbance Decoupling ◽  
Disturbance Decoupling ◽  
Mimo Nonlinear System ◽  
Network Application

Application of Multivariable Non-Linear Decoupling Control Based on Neural Network

2012 ◽  
Vol 214 ◽  
pp. 786-791
Author(s):  
Jian Bo Zhang ◽  
Dong Hai Fan ◽  
Ren Zhi Hu
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Distributed Control ◽  
Decoupling Control ◽  
Distributed Control System ◽  
Neural Network Controller ◽  
Mimo Nonlinear System ◽  
The Neural Network ◽  
Network Controller ◽  
The Mathematical Model

Aimed at Neural Network can approach any nonlinear system with arbitrary accuracy, the frame of distributed NN decoupling system are proposed to decouple the MIMO nonlinear system. In this paper, we designed and finished the Distributed Control System based on ABB’s Freelance 800F, and collected experimental data to model the thermostatic heater, then we have carried out the mathematical model by means of MATLAB dynamic simulation. In sequence, we trained the neural network controller in MATLAB. When the decoupling is completed, we used controller to control the MIMO nonlinear system in DCS. Experiment result shows that it is conscientiously feasible and deserves to be widely applied in the process of controlling industry.

Feedback linearization and feedforward neural network control with application to twin rotor mechanism

2016 ◽  
Vol 40 (2) ◽  
pp. 351-362 ◽  
Author(s):  
Chia-Wei Lin ◽  
Tzuu-Hseng S Li ◽  
Chung-Cheng Chen
Keyword(s):  
Neural Network ◽  
Feedback Linearization ◽  
Controller Design ◽  
Coupling Effect ◽  
Network Control ◽  
Feedforward Neural Network ◽  
Neural Network Control ◽  
Almost Disturbance Decoupling ◽  
Neural Network Controller ◽  
Twin Rotor

A twin rotor multi-input multi-output system (TRMMS) is a high-order nonlinear system with a significant cross-coupling effect. The control of TRMMSs is considered a markedly challenging topic in the field of robust control. This study proposes a novel feedback linearization and feedforward neural network controller design for a TRMMS with almost disturbance decoupling (ADD) capabilities. The proposed composite controller achieves exponentially global stability and ADD performance without applying any traditional parallel learning algorithms. This study proposes an organization of the feedforward neural network and the weights among the layers to guarantee the stability of the overall system. A number of nonlinear systems, which are too complex to be solved by general ADD studies, are proposed in this study to demonstrate that the proposed methodology can effectively achieve the tracking and ADD performances through Matlab. Moreover, an efficient algorithm is proposed for designing the feedback linearization and feedforward neural network control with ADD and tracking capabilities.

scholarly journals Controller Design of Multiinput Multioutput Time-Delay Large-Scale System

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Chia-Wei Lin ◽  
Tzuu-Hseng S. Li ◽  
Chung-Cheng Chen
Keyword(s):  
Time Delay ◽  
Feedback Linearization ◽  
Large Scale ◽  
Stability Characteristic ◽  
Test Bed ◽  
Pendulum System ◽  
Large Scale Systems ◽  
Almost Disturbance Decoupling ◽  
Inverted Pendulum System ◽  
Disturbance Decoupling

The paper presents a novel feedback linearization controller of nonlinear multiinput multioutput time-delay large-scale systems to obtain both the tracking and almost disturbance decoupling (ADD) performances. The significant contribution of this paper is to build up a control law such that the overall closed-loop system is stable for given initial condition and bounded tracking trajectory with the input-to-state-stability characteristic and almost disturbance decoupling performance. We have simulated the two-inverted-pendulum system coupled by a spring for networked control systems which has been used as a test bed for the study of decentralized control of large-scale systems.

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