Intelligent Neuro-Fuzzy Application in Semi-Active Suspension System

In the field of artificial intelligence, Neuro-Fuzzy (NF) refers to combinations of artificial neural networks and fuzzy logic and first time introduced in 1990s. Neuro-fuzzy results in a intelligent system that synergizes these two techniques by combining the human-like reasoning style of fuzzy systems with the learning and connectionist structure of neural networks. NF is widely termed as Fuzzy Neural Network (FNN) or Neuro-Fuzzy System (NFS) in the literature. NFS (the more popular term is used henceforth) incorporates the human-like reasoning style of fuzzy systems through the use of fuzzy sets and a linguistic model consisting of a set of IF-THEN fuzzy rules. The main strength of neuro-fuzzy systems is that they are universal approximations with the ability to solicit interpretable IF-THEN rules.

[1]  Kum-Gil Sung,et al.  Vibration control of vehicle ER suspension system using fuzzy moving sliding mode controller , 2008 .

[2]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[3]  Qingmei Yang,et al.  Modeling and Intelligent Control of Vehicle Active Suspension System , 2008, 2008 IEEE Conference on Robotics, Automation and Mechatronics.

[4]  J. Yen,et al.  Fuzzy Logic: Intelligence, Control, and Information , 1998 .

[5]  Seyed Hossein Sadati,et al.  Designing a Neuro-Fuzzy Controller for a Vehicle Suspension System, Using Feedback Error Learning , 2008 .

[6]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[7]  Ahsan Kareem,et al.  Feedback–feedforward control of offshore platforms under random waves , 2001 .

[8]  Ali Fellah Jahromi,et al.  Linear quadratic Gaussian application and clipped optimal algorithm using for semi active vibration of passenger car , 2011, 2011 IEEE International Conference on Mechatronics.

[9]  P. N. Roschke,et al.  Fuzzy modeling of a magnetorheological damper using ANFIS , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[10]  Hyun-Moo Koh,et al.  Semi-active fuzzy control of cable-stayed bridges using magneto-rheological dampers , 2007 .

[11]  M R Akbarzadeh Toutounchi,et al.  Semi-active control of structures using a neuro-inverse model of MR dampers , 2009 .

[12]  W. Melek,et al.  Intelligent Control of Vehicle Semi-Active Suspension Systems for improved Ride Comfort and Road Handling , 2006, NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society.

[13]  Chuen-Tsai Sun,et al.  Neuro-fuzzy And Soft Computing: A Computational Approach To Learning And Machine Intelligence [Books in Brief] , 1997, IEEE Transactions on Neural Networks.

[14]  Chih-Chen Chang,et al.  NEURAL NETWORK EMULATION OF INVERSE DYNAMICS FOR A MAGNETORHEOLOGICAL DAMPER , 2002 .

[15]  Hao Wang,et al.  The Neuro-fuzzy Identification of MR Damper , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

[16]  Şahin Yildirim,et al.  Vibration analysis of an experimental suspension system using artificial neural networks , 2009 .

[17]  Ali Fellah Jahromi,et al.  Linear Quadratic Regulator and Fuzzy controller application in full-car model of suspension system with Magnetorheological shock absorber , 2010, Proceedings of 2010 IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications.

[18]  Paul N. Roschke,et al.  Neuro‐Fuzzy Control of Railcar Vibrations Using Semiactive Dampers , 2004 .

[19]  Lily L. Zhou,et al.  Integrated fuzzy logic and genetic algorithms for multi-objective control of structures using MR dampers , 2006 .

[20]  Zhao-Dong Xu,et al.  Neuro-fuzzy control strategy for earthquake-excited nonlinear magnetorheological structures , 2008 .

[21]  A. Khajekaramodin H. Haji,et al.  Semi-active Control of Structures Using Neuro-Inverse Model of MR Dampers , 2007 .

[22]  Jan Jantzen,et al.  Foundations of fuzzy control , 2007 .