Modeling and control of non-linear systems using soft computing techniques

Denaï, Mouloud; Палис, Ф.; Zeghbib, Abdelhafid

doi:10.1016/j.asoc.2005.12.005

Cited by 84 publications

(51 citation statements)

References 4 publications

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“…In the second or backward pass the patterns are propagated again, and in this epoch, back propagation is used to modify the premise parameters, while the consequent parameters remain fixed. This procedure is then iterated until the error criterion is satisfied (Denai et al 2007). …”

Section: Adaptive-network-based Fuzzy Inference System (Anfis)mentioning

confidence: 99%

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

et al. 2010

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Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model.

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Section: Adaptive-network-based Fuzzy Inference System (Anfis)mentioning

confidence: 99%

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

et al. 2010

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“…The past few years have witnessed a speedy growth in the number and various applications of fuzzy logic [13][14][15][16][17][18][19].…”

Section: Inverted Pendulum On a Cartmentioning

confidence: 99%

Control of Non-Linear Inverted Pendulum using Fuzzy Logic Controller

Jain¹,

Tayal²,

Sehgal³

2013

IJCA

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This paper proposes an intelligent control approach towards Inverted Pendulum in mechanical engineering. Inverted Pendulum is a well known topic in process control and offering a diverse range of research in the area of the mechanical and control engineering. Fuzzy controller is an intelligent controller based on the model of fuzzy logic i.e. it does not require accurate mathematical modelling of the system nor complex computations and it can handle complex and non linear systems without linearization. Our objective is to implement a Fuzzy based controller and demonstrate its application to Inverted Pendulum. Model design and simulation are done in MATLAB SIMULINK ® software.

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“…The structure of ANN based nonlinear IMC i.e. IMC for nonlinear system is presented by researchers 1,2,8 . For nonlinear IMC, it is not easy to design the ANN and ANFIS based internal model (IM) and its inverse, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years intelligent soft computing techniques such as fuzzy inference system (FIS), artificial neural network (ANN), and Adaptive neuro-fuzzy inference system (ANFIS) are widely used in various scientific domains as a powerful tool of identification, modeling and control of highly nonlinear and complex systems. Among these techniques ANFIS is one of the best tradeoff between neural and fuzzy systems; it provides smoothness and knowledge representation due to the fuzzy logic and adaptability capability due to the ANN [2][3][4][5] . Standard improved internal model control (S-IMC) is a effective strategy in process control and it was introduced by Garcia and Morari 6 .…”

Section: Introductionmentioning

confidence: 99%

Neuro-Fuzzy-based Improved IMC for Speed Control of Nonlinear Heavy Duty Vehicles

Yadav

Gaur

2016

Def. Sc. Jl.

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A neuro-fuzzy based improved internal model control (I-IMC) is proposed for speed control of uncertain nonlinear heavy duty vehicle (HDV) as the standard IMC (S-IMC) can't tackle the nonlinear systems effectively and degrades the performance of HDV system. Adaptive neuro-fuzzy inference system and artificial neural network with adaptive control are used for the design of I-IMC. The proposed control techniques are developed to achieve the better speed tracking performance and robustness of HDV system under the influence of road grade disturbance.

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Modeling and control of non-linear systems using soft computing techniques

Cited by 84 publications

References 4 publications

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Control of Non-Linear Inverted Pendulum using Fuzzy Logic Controller

Neuro-Fuzzy-based Improved IMC for Speed Control of Nonlinear Heavy Duty Vehicles

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