Modeling and control of complex nonlinear systems based on TS model

2012 ◽  
Author(s):  
Zhao Yuchi ◽  
Liu Jing
Keyword(s):  
Nonlinear Systems ◽  
Modeling And Control ◽  
Complex Nonlinear Systems ◽  
And Control

Modeling and Control of Complex Nonlinear Systems Based on TS Model

2013 ◽  
Vol 380-384 ◽  
pp. 417-420
Author(s):  
Yu Chi Zhao ◽  
Jing Liu
Keyword(s):  
System Theory ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Fuzzy Model ◽  
Modeling And Control ◽  
Linear Control Theory ◽  
Complex Nonlinear Systems ◽  
And Control

The current theory of nonlinear systems is still not perfect. The modeling and control of nonlinear system problem has always been the difficulty. In a variety of methods of its study, fuzzy system theory because of having the language descriptive way similar to the human mind, can obtain and deal with the qualitative information intelligently. The theory itself also has non-linear characteristics. Therefore the use of fuzzy systems theory to establish the fuzzy model of nonlinear system can well describe the nonlinear characteristics. T-S fuzzy systems, due to the combination of the good performance of the fuzzy system to deal with nonlinear problems with the simple linear expressions, are not only suitable for modeling the nonlinear system, but also use T-S fuzzy model and the linear control theory method to design the controller. So it has been widely used in nonlinear system control problems, and has also greatly developed the T-S fuzzy system theory, appearing a lot of methods of structural and parameter identification. However, this study of T-S fuzzy rules makes us have to face the difference of different ways to select the number of rules as well as online self-adaptability of the number of rules which off-line method lacks when using T-S fuzzy model to deal with nonlinear system modeling and control problem. In view of this, this paper researches on modeling and controlling of complex nonlinear systems based on TS model from different perspectives.

Fuzzy modeling and control of a class of non‐differentiable multi‐input multi‐output nonlinear systems

2021 ◽  
Author(s):  
Kazem Zare ◽  
Mokhtar Shasadeghi ◽  
Afshin Izadian ◽  
Taher Niknam ◽  
Mohammad Hassan Asemani
Keyword(s):  
Nonlinear Systems ◽  
Fuzzy Modeling ◽  
Modeling And Control ◽  
And Control

An Optimization Procedure of Model’s Base Construction in Multimodel Representation of Complex Nonlinear Systems

2021 ◽  
Author(s):  
Bennasr Hichem ◽  
M’Sahli Faouzi
Keyword(s):  
Nonlinear Systems ◽  
Learning Algorithm ◽  
Batch Reactor ◽  
Optimization Procedure ◽  
Base Number ◽  
Deterministic Approach ◽  
Modeling Analysis ◽  
Complex Nonlinear Systems ◽  
Definition Of ◽  
And Control

The multimodel approach is a research subject developed for modeling, analysis and control of complex systems. This approach supposes the definition of a set of simple models forming a model’s library. The number of models and the contribution of their validities is the main issues to consider in the multimodel approach. In this chapter, a new theoretical technique has been developed for this purpose based on a combination of probabilistic approaches with different objective function. First, the number of model is constructed using neural network and fuzzy logic. Indeed, the number of models is determined using frequency-sensitive competitive learning algorithm (FSCL) and the operating clusters are identified using Fuzzy K- means algorithm. Second, the Models’ base number is reduced. Focusing on the use of both two type of validity calculation for each model and a stochastic SVD technique is used to evaluate their contribution and permits the reduction of the Models’ base number. The combination of FSCL algorithms, K-means and the SVD technique for the proposed concept is considered as a deterministic approach discussed in this chapter has the potential to be applied to complex nonlinear systems with dynamic rapid. The recommended approach is implemented, reviewed and compared to academic benchmark and semi-batch reactor, the results in Models’ base reduction is very important witch gives a good performance in modeling.

scholarly journals A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Yiming Jiang ◽  
Chenguang Yang ◽  
Jing Na ◽  
Guang Li ◽  
Yanan Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Human Robot Interaction ◽  
Learning Tools ◽  
Robot Interaction ◽  
Practical Applications ◽  
Powerful Learning ◽  
Wide Range ◽  
Complex Nonlinear Systems ◽  
And Control

As an imitation of the biological nervous systems, neural networks (NNs), which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. This article aims to bring a brief review of the state-of-the-art NNs for the complex nonlinear systems by summarizing recent progress of NNs in both theory and practical applications. Specifically, this survey also reviews a number of NN based robot control algorithms, including NN based manipulator control, NN based human-robot interaction, and NN based cognitive control.

Dynamic Neural Networks for Modeling and Control of Nonlinear Systems

2003 ◽  
Vol 9 (2) ◽  
pp. 61-70 ◽  
Author(s):  
Farzad Pourboghrat ◽  
Harin Pongpairoj ◽  
Ziqian Liu ◽  
Farshad Farid ◽  
Farhang Pourboghrat ◽  
...  
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Dynamic Neural Networks ◽  
Modeling And Control ◽  
And Control
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