Stability and Almost Disturbance Decoupling Analysis of Nonlinear System Subject to Feedback Linearization and Feedforward Neural Network Controller

2008 ◽  
Vol 19 (7) ◽  
pp. 1220-1230 ◽  
Author(s):  
Ting-Li Chien ◽  
Chung-Cheng Chen ◽  
Yi-Chieh Huang ◽  
Wen-Jiun Lin
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Feedback Linearization ◽  
Feedforward Neural Network ◽  
Almost Disturbance Decoupling ◽  
Neural Network Controller ◽  
Disturbance Decoupling ◽  
Network Controller ◽  
Decoupling Analysis

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.

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.

scholarly journals Tuning Artificial Neural Network Controller Using Particle Swarm Optimization Technique for Nonlinear System

2021 ◽  
Author(s):  
Sabrine Slama ◽  
Ayachi Errachdi ◽  
Mohamed Benrejeb
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Nonlinear System ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Neural Network Controller ◽  
Network Controller ◽  
Artificial Neural

This chapter proposes an optimization technique of Artificial Neural Network (ANN) controller, of single-input single-output time-varying discrete nonlinear system. A bio-inspired optimization technique, Particle Swarm Optimization (PSO), is proposed to be applied in ANN to avoid any possibilities from local extreme condition. Further, a PSO based neural network controller is also developed to be integrated with the designed system to control a nonlinear systems. The simulation results of an example of nonlinear system demonstrate the effectiveness of the proposed approach using Particle Swarm Optimization approach in terms of reduced oscillations compared to classical neural network optimization method. MATLAB was used as simulation tool.

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