Evolutionary algorithm-based design of a fuzzy TBF predictive model and TSK fuzzy anti-sway crane control system

The efficiency of material handling system requires an automation on the different levels of control and supervision to keep availability of the material handling devices for fast, safety and precise transferring materials, as well as to reduce the maintenance cost, which is involved by enhancing the productivity of manufacturing process. In this paper, evolutionary-based algorithm for fuzzy logic-based data-driven predictive model of time between failures (TBF) and adaptive crane control system design is proposed. The heuristic searching strategy combining the arithmetical crossover, uniform and non-uniform mutation and deletion/insertion mutation is developed for optimizing the rules base (RB) and tuning the triangular-shaped membership functions to increase the efficiency and accuracy of a fuzzy rule-based system (FRBS). The evolutionary algorithm (EA) was employed to design a fuzzy predictive model based on the historical data of operational states monitored between the failures of the laboratory scaled overhead traveling crane electronic equipment. The fuzzy predictive model of TBF was implemented in the supervisory system created for supporting decision-making process through forecasting upcoming failure and delivering the user-defined maintenance strategies. The effectiveness of EA was also verified through designing a Takagi-Sugeno-Kang (TSK) fuzzy controller in the anti-sway crane control system.

[1]  Y. Kijima,et al.  An optimization of fuzzy controller and it's application to overhead crane , 1995, Proceedings of IECON '95 - 21st Annual Conference on IEEE Industrial Electronics.

[2]  Ranjan Ganguli,et al.  Genetic fuzzy system for online structural health monitoring of composite helicopter rotor blades , 2007 .

[3]  Ronald R. Yager,et al.  Essentials of fuzzy modeling and control , 1994 .

[4]  John H. Holland,et al.  COGNITIVE SYSTEMS BASED ON ADAPTIVE ALGORITHMS1 , 1978 .

[5]  Seyed Jamshid Mousavi,et al.  A hybrid genetic algorithm-adaptive network-based fuzzy inference system in prediction of wave parameters , 2009, Eng. Appl. Artif. Intell..

[6]  Paul M. Frank,et al.  Process supervision with the aid of fuzzy logic , 1993, Proceedings of IEEE Systems Man and Cybernetics Conference - SMC.

[7]  Seok-Beom Roh,et al.  Parameter estimation of fuzzy controller and its application to inverted pendulum , 2004, Eng. Appl. Artif. Intell..

[8]  G. L. Gissinger,et al.  Fuzzy control of an overhead crane performance comparison with classic control , 1995 .

[9]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  Abdollah Homaifar,et al.  Genetic algorithm based gain scheduling , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[11]  Jianqiang Yi,et al.  Proposal of GA-based two-stage fuzzy control of overhead crane , 2002, 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering. TENCOM '02. Proceedings..

[12]  Kevin M. Passino,et al.  Expert supervision of fuzzy learning systems for fault tolerant aircraft control , 1995 .

[13]  Enrico Zio,et al.  A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system , 2010, Reliab. Eng. Syst. Saf..

[14]  Kenneth A. De Jong,et al.  Using genetic algorithms for concept learning , 1993, Machine Learning.

[15]  Benoît Iung,et al.  Dynamic behavioural model for assessing impact of regeneration actions on system availability: Application to weapon systems , 2011, Reliab. Eng. Syst. Saf..

[16]  Zbigniew Michalewicz,et al.  Handling Constraints in Genetic Algorithms , 1991, ICGA.

[17]  Ozgur Kisi,et al.  Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models , 2011 .

[18]  Stephen F. Smith,et al.  A learning system based on genetic adaptive algorithms , 1980 .

[19]  Maysam F. Abbod,et al.  Fuzzy Logic-Based Anti-Sway Control Design for Overhead Cranes , 2000, Neural Computing & Applications.

[20]  Eduardo Julio Moya de la Torre,et al.  Fault detection and fuzzy rule extraction in AC motors by a neuro-fuzzy ART-based system , 2005, Eng. Appl. Artif. Intell..

[21]  Chulhyun Kim,et al.  Forecasting time series with genetic fuzzy predictor ensemble , 1997, IEEE Trans. Fuzzy Syst..

[22]  Antonio González Muñoz,et al.  SLAVE: a genetic learning system based on an iterative approach , 1999, IEEE Trans. Fuzzy Syst..

[23]  Yixin Diao,et al.  Stable fault-tolerant adaptive fuzzy/neural control for a turbine engine , 2001, IEEE Trans. Control. Syst. Technol..

[24]  Wen Yu,et al.  Stable adaptive compensation with fuzzy CMAC for an overhead crane , 2011, Inf. Sci..

[25]  Kazuya Ogata,et al.  Adaptive Control of a Planar Gantry Crane by the Switching of Controllers , 1999 .

[26]  Joseph Aguilar-Martin,et al.  Automaton based on fuzzy clustering methods for monitoring industrial processes , 2013, Eng. Appl. Artif. Intell..

[27]  Jaroslaw Smoczek,et al.  Supervisory System for Supporting Decision-Making Process in Proactive Maintenance of Technical Object , 2012 .

[28]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[29]  Bogdan Filipič,et al.  A combined machine learning and genetic algorithm approach to controller design , 1999 .

[30]  Jyoti Kiran,et al.  Data Clustering Approach to Industrial Process Monitoring, Fault Detection and Isolation , 2011 .

[31]  J.B. Theocharis,et al.  A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation , 2004, IEEE Transactions on Energy Conversion.

[32]  Gary M. Weiss.  Timeweaver: a genetic algorithm for identifying predictive patterns in sequences of events , 1999 .

[33]  Jaroslaw Smoczek,et al.  Fuzzy logic approach to the gain scheduling crane control system , 2010, 2010 15th International Conference on Methods and Models in Automation and Robotics.

[34]  R. Szabo,et al.  Fuzzy systems for simulation and prediction of the residual life of insulating materials for electrical machines windings , 2004, Proceedings of the 2004 IEEE International Conference on Solid Dielectrics, 2004. ICSD 2004..

[35]  E. H. K. Fung,et al.  Intelligent Automatic Fault Detection for Actuator Failures in Aircraft , 2009, IEEE Transactions on Industrial Informatics.

[36]  Hisao Ishibuchi,et al.  Multiobjective Genetic Fuzzy Systems: Review and Future Research Directions , 2007, 2007 IEEE International Fuzzy Systems Conference.

[37]  Alireza Sadeghian,et al.  Electrical machine fault detection using adaptive neuro-fuzzy inference , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[38]  Francisco Herrera,et al.  Genetic fuzzy systems: taxonomy, current research trends and prospects , 2008, Evol. Intell..

[39]  Nasser Sadati,et al.  Design of a Gain-Scheduling Anti-Swing Controller for Tower Cranes Using Fuzzy Clustering Techniques , 2006, 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06).

[40]  Jaroslaw Smoczek,et al.  A MECHATRONICS APPROACH IN INTELLIGENT CONTROL SYSTEMS OF THE OVERHEAD TRAVELING CRANES PROTOTYPING , 2008 .

[41]  Francisco Herrera,et al.  Ten years of genetic fuzzy systems: current framework and new trends , 2004, Fuzzy Sets Syst..

[42]  Abdollah Homaifar,et al.  Genetic algorithms solution for unconstrained optimal crane control , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[43]  Juan A. Méndez,et al.  An Application of a Neural Self-Tuning Controller to an Overhead Crane , 1999, Neural Computing & Applications.

[44]  Cheng-Yuan Chang,et al.  Adaptive Fuzzy Controller of the Overhead Cranes With Nonlinear Disturbance , 2007, IEEE Transactions on Industrial Informatics.

[45]  Gilles Venturini,et al.  SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts , 1993, ECML.

[46]  Jamil M. Renno,et al.  Generalized Design of an Anti-swing Fuzzy Logic Controller for an Overhead Crane with Hoist , 2008 .

[47]  Jaroslaw Smoczek,et al.  Design of gain scheduling anti-sway crane controler using genetic fuzzy system , 2012, 2012 17th International Conference on Methods & Models in Automation & Robotics (MMAR).

[48]  Francisco Herrera,et al.  A proposal for improving the accuracy of linguistic modeling , 2000, IEEE Trans. Fuzzy Syst..

[49]  Qi Yong,et al.  Software Aging Prediction Model Based on Fuzzy Wavelet Network with Adaptive Genetic Algorithm , 2006, 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06).

[50]  H. Kinjo,et al.  Load Swing Suppression in Jib Crane Systems Using a Genetic Algorithm-trained Neuro-controller , 2007, 2007 IEEE International Conference on Mechatronics.

[51]  Joseph Aguilar-Martin,et al.  Process situation assessment: From a fuzzy partition to a finite state machine , 2006, Eng. Appl. Artif. Intell..