An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

An overview on recent developments in fault diagnosis is carried out.Machine learning, data mining and evolving soft computing techniques are discussed.Real liquid level control, wind turbine and servo system applications are offered.An overview on nature-inspired optimal control of industrial processes is given.New research challenges with strong industrial impact are highlighted. Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.

[1]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[2]  Tommy W. S. Chow,et al.  Weighted local and global regressive mapping: A new manifold learning method for machine fault classification , 2014, Eng. Appl. Artif. Intell..

[3]  Reuven Y. Rubinstein,et al.  Optimization of computer simulation models with rare events , 1997 .

[4]  Lixin Tang,et al.  An Improved Differential Evolution Algorithm for Practical Dynamic Scheduling in Steelmaking-Continuous Casting Production , 2014, IEEE Transactions on Evolutionary Computation.

[5]  Michael G. Safonov,et al.  The unfalsified control concept and learning , 1997 .

[6]  Siamak Talatahari,et al.  A CHARGED SYSTEM SEARCH WITH A FLY TO BOUNDARY METHOD FOR DISCRETE OPTIMUM DESIGN OF TRUSS STRUCTURES , 2010 .

[7]  Zsolt Csaba Johanyák,et al.  Fuzzy Modeling of Thermoplastic Composites' Melt Volume Rate , 2014, Comput. Informatics.

[8]  V. Sugumaran,et al.  Fault diagnosis of monoblock centrifugal pump using SVM , 2014 .

[9]  Plamen Angelov.  Autonomous Learning Systems:From Data to Knowledge in Real Time , 2012 .

[10]  Plamen P. Angelov,et al.  Evolving Fuzzy-Rule-Based Classifiers From Data Streams , 2008, IEEE Transactions on Fuzzy Systems.

[11]  Shuzhi Sam Ge,et al.  Data Driven Adaptive Predictive Control for Holonomic Constrained Under-Actuated Biped Robots , 2012, IEEE Transactions on Control Systems Technology.

[12]  Adnan Yassine,et al.  Hybrid Genetic Simulated Annealing Algorithm (HGSAA) to Solve Storage Container Problem in Port , 2012, ACIIDS.

[13]  Stefan Preitl,et al.  Stability and Sensitivity Analysis of Fuzzy Control Systems. Mechatronics Applications , 2006 .

[14]  Oscar Castillo,et al.  Optimization of interval type-2 fuzzy logic controllers using evolutionary algorithms , 2011, Soft Comput..

[15]  Kenzo Nonami,et al.  Optimal two-degree-of-freedom fuzzy control for locomotion control of a hydraulically actuated hexapod robot , 2007, Inf. Sci..

[16]  Paul Fleming,et al.  Use of SCADA Data for Failure Detection in Wind Turbines , 2011 .

[17]  Shailesh Tiwari,et al.  Physics-Inspired Optimization Algorithms: A Survey , 2013 .

[18]  Fredrik Gustafsson,et al.  Adaptive filtering and change detection , 2000 .

[19]  József K. Tar,et al.  On the design of an obstacle avoiding trajectory: Method and simulation , 2009, Math. Comput. Simul..

[20]  Shiva Gholami-Boroujeny,et al.  Active noise control using an adaptive bacterial foraging optimization algorithm , 2014, Signal Image Video Process..

[21]  Jianzhong Zhou,et al.  Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis. , 2014, ISA transactions.

[22]  Peter Fogh Odgaard,et al.  Unknown input observer based detection of sensor faults in a wind turbine , 2010, 2010 IEEE International Conference on Control Applications.

[23]  Antonio Berlanga,et al.  Multiobjective Local Search as an Initialization Procedure for Evolutionary Approaches to Polygonal Approximation , 2014 .

[24]  J Richalet,et al.  An approach to predictive control of multivariable time-delayed plant: stability and design issues. , 2004, ISA transactions.

[25]  Sebastian Engell,et al.  Automatic controller tuning via unfalsified control , 2012 .

[26]  Marius-Lucian Tomescu,et al.  Fuzzy Logic Control System Stability Analysis Based on Lyapunov's Direct Method , 2009, Int. J. Comput. Commun. Control.

[27]  Raymond Chiong,et al.  Nature-Inspired Algorithms for Optimisation , 2009, Nature-Inspired Algorithms for Optimisation.

[28]  Balasaheb M. Patre,et al.  A survey on sliding mode control strategies for induction motors , 2013, Annu. Rev. Control..

[29]  Chang-Ming Liaw,et al.  Fuzzy two-degrees-of-freedom speed controller for motor drives , 1995, IEEE Trans. Ind. Electron..

[30]  Magnus Löfstrand,et al.  Addressing concept drift to improve system availability by updating one-class data-driven models , 2015, Evol. Syst..

[31]  Antonio Sala,et al.  Relaxed LMI conditions for closed-loop fuzzy systems with tensor-product structure , 2007, Eng. Appl. Artif. Intell..

[32]  Eneko Osaba,et al.  AMCPA: A Population Metaheuristic With Adaptive Crossover Probability and Multi-Crossover Mechanism for Solving Combinatorial Optimization Problems , 2014 .

[33]  Milos Manic,et al.  Uncertainty-Robust Design of Interval Type-2 Fuzzy Logic Controller for Delta Parallel Robot , 2011, IEEE Transactions on Industrial Informatics.

[34]  R. Garduno-Ramirez,et al.  2 DOF Fuzzy Gain-Scheduling PI for Combustion Turbogenerator Speed Control , 2012 .

[35]  Claudia-Adina Dragos,et al.  Novel Tensor Product Models for Automatic Transmission System Control , 2012, IEEE Systems Journal.

[36]  Somyot Kaitwanidvilai,et al.  Robust loop shaping–fuzzy gain scheduling control of a servo-pneumatic system using particle swarm optimization approach , 2011 .

[37]  Gilberto Reynoso-Meza,et al.  Controller tuning using evolutionary multi-objective optimisation: Current trends and applications , 2014 .

[38]  Rodolfo E. Haber,et al.  Using Simulated Annealing for Optimal Tuning of a PID Controller for Time-Delay Systems. An Application to a High-Performance Drilling Process , 2007, IWANN.

[39]  Agustín Gajate,et al.  Intelligent Tuning of Fuzzy Controllers by Learning and Optimization , 2014 .

[40]  Radu-Emil Precup,et al.  A survey on industrial applications of fuzzy control , 2011, Comput. Ind..

[41]  Plamen P. Angelov,et al.  A new unsupervised approach to fault detection and identification , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[42]  Shangtai Jin,et al.  Data-Driven Model-Free Adaptive Control for a Class of MIMO Nonlinear Discrete-Time Systems , 2011, IEEE Transactions on Neural Networks.

[43]  Amparo Alonso-Betanzos,et al.  Automatic bearing fault diagnosis based on one-class ν-SVM , 2013, Comput. Ind. Eng..

[44]  A. Kaveh,et al.  A novel heuristic optimization method: charged system search , 2010 .

[45]  Emanuel-Florin Iftene,et al.  Efficiency of a Combined Protection Method against Correlation , 2014, Int. J. Comput. Commun. Control.

[46]  Antonio Visioli.  A new design for a PID plus feedforward controller , 2004 .

[47]  Cédric Join,et al.  Revisiting some practical issues in the implementation of model-free control , 2011 .

[48]  Stefan Preitl,et al.  Novel Adaptive Charged System Search algorithm for optimal tuning of fuzzy controllers , 2014, Expert Syst. Appl..

[49]  Imre J. Rudas,et al.  ANFIS-based Wireless Sensor Network (WSN) Applications for Air Conditioner Control , 2013 .

[50]  S. Preitl,et al.  On the combination of tensor product and fuzzy models , 2008, 2008 IEEE International Conference on Automation, Quality and Testing, Robotics.

[51]  A. Kusiak,et al.  Monitoring Wind Farms With Performance Curves , 2013, IEEE Transactions on Sustainable Energy.

[52]  Broderick Crawford,et al.  Combining Tabu Search and Genetic Algorithms to Solve the Capacitated Multicommodity Network Flow Problem , 2014 .

[53]  Claudia-Adina Dragos,et al.  Iterative performance improvement of fuzzy control systems for three tank systems , 2012, Expert Syst. Appl..

[54]  Siamak Talatahari,et al.  Optimal design of skeletal structures via the charged system search algorithm , 2010 .

[55]  M. Marchesoni,et al.  Self-commissioning of direct drive systems , 2012, International Symposium on Power Electronics Power Electronics, Electrical Drives, Automation and Motion.

[56]  József K. Tar,et al.  Generic two-degree-of-freedom linear and fuzzy controllers for integral processes , 2009, J. Frankl. Inst..

[57]  Stefan Preitl,et al.  Iterative Feedback Tuning in Fuzzy Control Systems. Theory and Applications , 2006 .

[58]  Madhav J. Nigam,et al.  Applications of quantum inspired computational intelligence: a survey , 2014, Artificial Intelligence Review.

[59]  Oscar Castillo,et al.  A review on interval type-2 fuzzy logic applications in intelligent control , 2014, Inf. Sci..

[60]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[61]  David G. Stork,et al.  Pattern Classification , 1973 .

[62]  E. Daryabeigi,et al.  Smart bacterial foraging algorithm based controller for speed control of switched reluctance motor drives , 2014 .

[63]  Kauko Leiviskä,et al.  Large-Scale Complex Systems , 2009, Handbook of Automation.

[64]  Kevin M. Passino,et al.  Bacterial Foraging Optimization , 2010, Int. J. Swarm Intell. Res..

[65]  Magnus Mossberg,et al.  Iterative feedback tuning of PID parameters: comparison with classical tuning rules , 2003 .

[66]  Abdul Qayyum Khan,et al.  Observer-based FDI Schemes for Wind Turbine Benchmark , 2011 .

[67]  Arunava Chatterjee,et al.  A Gravitational Search Algorithm (GSA) based Photo-Voltaic (PV) excitation control strategy for single phase operation of three phase wind-turbine coupled induction generator , 2014 .

[68]  Wenxian Yang,et al.  Wind turbine condition monitoring by the approach of SCADA data analysis , 2013 .

[69]  Anula Khare,et al.  A review of particle swarm optimization and its applications in Solar Photovoltaic system , 2013, Appl. Soft Comput..

[70]  Yang Yang,et al.  Modeling and Solution for the Coil Sequencing Problem in Steel Color-Coating Production , 2012, IEEE Transactions on Control Systems Technology.

[71]  Mayorkinos Papaelias,et al.  Condition monitoring of wind turbines: Techniques and methods , 2012 .

[72]  Oscar Castillo,et al.  A review on the design and optimization of interval type-2 fuzzy controllers , 2012, Appl. Soft Comput..

[73]  Nadia Nedjah,et al.  Multiobjective Gaussian Particle Swarm Approach Applied to Multi-loop PI Controller Tuning of a Quadruple-Tank System , 2010, Multi-Objective Swarm Intelligent System.

[74]  R. Precup,et al.  Stability analysis method for fuzzy control systems dedicated controlling nonlinear processes , 2007 .

[75]  Alireza Karimi,et al.  Model-Free Precompensator Tuning Based on the Correlation Approach , 2008, IEEE Transactions on Control Systems Technology.

[76]  Gyula Hermann,et al.  Robust Convex Hull-based Algoritm for Straightness and Flatness Determination in Coordinate Measuring , 2007 .

[77]  Stefan Preitl,et al.  Fuzzy controllers for tire slip control in anti-lock braking systems , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[78]  Meik Schlechtingen,et al.  Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection , 2011 .

[79]  Darko Bozanic,et al.  Green logistic vehicle routing problem: Routing light delivery vehicles in urban areas using a neuro-fuzzy model , 2014, Expert Syst. Appl..

[80]  Kevin Kok Wai Wong,et al.  Fuzzy Rule Interpolation and Extrapolation Techniques: Criteria and Evaluation Guidelines , 2011, J. Adv. Comput. Intell. Intell. Informatics.

[81]  Karim Salahshoor,et al.  Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers , 2010 .

[82]  Amitava Chatterjee,et al.  Fuzzy model predictive control of non-linear processes using convolution models and foraging algorithms , 2013 .

[83]  Dervis Karaboga,et al.  A survey: algorithms simulating bee swarm intelligence , 2009, Artificial Intelligence Review.

[84]  Stefan Preitl,et al.  Evolutionary optimization-based tuning of low-cost fuzzy controllers for servo systems , 2013, Knowl. Based Syst..

[85]  Rodolfo E. Haber,et al.  Optimal fuzzy control system using the cross-entropy method. A case study of a drilling process , 2010, Inf. Sci..

[86]  Igor Skrjanc,et al.  Direct fuzzy model‐reference adaptive control , 2002, Int. J. Intell. Syst..

[87]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[88]  Alessandro Pisano,et al.  On-line adaptive clustering for process monitoring and fault detection , 2012, Expert Syst. Appl..

[89]  Michal Pluhacek,et al.  On the behavior and performance of chaos driven PSO algorithm with inertia weight , 2013, Comput. Math. Appl..

[90]  L. Coelho,et al.  A novel chaotic particle swarm optimization approach using Hénon map and implicit filtering local search for economic load dispatch , 2009 .

[91]  Chia-Feng Juang,et al.  Evolutionary-Group-Based Particle-Swarm-Optimized Fuzzy Controller With Application to Mobile-Robot Navigation in Unknown Environments , 2011, IEEE Transactions on Fuzzy Systems.

[92]  Luigi Fortuna,et al.  Chaotic sequences to improve the performance of evolutionary algorithms , 2003, IEEE Trans. Evol. Comput..

[93]  Milos Manic,et al.  Interval Type-2 fuzzy voter design for fault tolerant systems , 2011, Inf. Sci..

[94]  J. Spall,et al.  Model-free control of nonlinear stochastic systems with discrete-time measurements , 1998, IEEE Trans. Autom. Control..

[95]  Gang Yin,et al.  Online fault diagnosis method based on Incremental Support Vector Data Description and Extreme Learning Machine with incremental output structure , 2014, Neurocomputing.

[96]  Peter J Seiler,et al.  Wind Turbine Fault Detection Using Counter-Based Residual Thresholding , 2011 .

[97]  A. J. McDaid,et al.  Control of IPMC Actuators for Microfluidics With Adaptive “Online” Iterative Feedback Tuning , 2012, IEEE/ASME Transactions on Mechatronics.

[98]  D. N. Tibarewala,et al.  Application of Swarm Intelligence Computation Techniques in PID Controller Tuning: A Review , 2012 .

[99]  Ján Vascák,et al.  Adaptation of fuzzy cognitive maps by migration algorithms , 2012, Kybernetes.

[100]  Plamen Angelov,et al.  Evolving Intelligent Systems, eIS , 2006 .

[101]  Pierluigi Siano,et al.  Designing fuzzy logic controllers for DC–DC converters using multi-objective particle swarm optimization , 2014 .

[102]  Adi Soeprijanto,et al.  Controlling chaos and voltage collapse using an ANFIS-based composite controller-static var compensator in power systems , 2013 .

[103]  Patricia Melin,et al.  Particle swarm optimization of interval type-2 fuzzy systems for FPGA applications , 2013, Appl. Soft Comput..

[104]  Hamid Baseri,et al.  Simulated annealing based optimization of dressing conditions for increasing the grinding performance , 2012 .

[105]  Ioan Dumitrache,et al.  INTELLIGENT TECHNIQUES FOR COGNITIVE MOBILE ROBOTS , 2004 .

[106]  Paul M. Frank,et al.  Fuzzy logic and neural network applications to fault diagnosis , 1997, Int. J. Approx. Reason..

[107]  Emil M. Petriu,et al.  Experiment-Based Teaching in Advanced Control Engineering , 2011, IEEE Transactions on Education.

[108]  Sami Othman,et al.  Support Vector Machines for Fault Detection in Wind Turbines , 2011 .

[109]  Håkan Hjalmarsson,et al.  Iterative feedback tuning—an overview , 2002 .

[110]  M. Chidambaram,et al.  Set-point weighted PID controllers for unstable systems , 2000, J. Frankl. Inst..

[111]  Miguel A. Olivares-Méndez,et al.  Cross-Entropy Optimization for Scaling Factors of a Fuzzy Controller: A See-and-Avoid Approach for Unmanned Aerial Systems , 2013, J. Intell. Robotic Syst..

[112]  Plamen Angelov,et al.  Autonomous Learning Systems: From Data Streams to Knowledge in Real-time , 2013 .

[113]  Lihong Qiao,et al.  A cross-entropy-based approach for the optimization of flexible process planning , 2013 .

[114]  Nader Meskin,et al.  Multiple sensor fault diagnosis by evolving data-driven approach , 2014, Inf. Sci..

[115]  T. W. Verbruggen,et al.  Wind Turbine Operation & Maintenance based on Condition Monitoring WT-Ω , 2003 .

[116]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[117]  Antonio Visioli,et al.  Fuzzy logic based set-point weight tuning of PID controllers , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[118]  Stefan Preitl,et al.  Charged System Search Algorithms for Optimal Tuning of PI Controllers , 2012, CESCIT.

[119]  Luca Maria Gambardella,et al.  A survey on metaheuristics for stochastic combinatorial optimization , 2009, Natural Computing.

[120]  Chih-Yung Chen,et al.  PID Controller Design for MIMO Processes Using Improved Particle Swarm Optimization , 2014, Circuits Syst. Signal Process..

[121]  Biao Huang,et al.  A data driven subspace approach to predictive controller design , 2001 .

[122]  Rodolfo E. Haber,et al.  An optimal fuzzy control system in a network environment based on simulated annealing. An application to a drilling process , 2009, Appl. Soft Comput..

[123]  Shangtai Jin,et al.  A Novel Data-Driven Control Approach for a Class of Discrete-Time Nonlinear Systems , 2011, IEEE Transactions on Control Systems Technology.

[124]  Claudia-Adina Dragos,et al.  Tensor product-based real-time control of the liquid levels in a three tank system , 2010, 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[125]  B. S. J. Costa,et al.  A multistage fuzzy controller: Toolbox for industrial applications , 2012, 2012 IEEE International Conference on Industrial Technology.

[126]  József K. Tar,et al.  Optimal Control Systems with Reduced Parametric Sensitivity Based on Particle Swarm Optimization and Simulated Annealing , 2011, Intelligent Computational Optimization in Engineering.

[127]  Kaoru Hirota,et al.  Integrated Decision-Making System for Robot Soccer , 2011, J. Adv. Comput. Intell. Intell. Informatics.

[128]  Stefan Preitl,et al.  An extension of tuning relations after symmetrical optimum method for PI and PID controllers , 1999, Autom..

[129]  Stefan Preitl,et al.  Novel Adaptive Gravitational Search Algorithm for Fuzzy Controlled Servo Systems , 2012, IEEE Transactions on Industrial Informatics.

[130]  Stefan Preitl,et al.  Gravitational search algorithm-based design of fuzzy control systems with a reduced parametric sensitivity , 2013, Inf. Sci..

[131]  Azah Mohamed,et al.  Gravitational search algorithm for coordinated design of PSS and TCSC as damping controller , 2012 .

[132]  Pagavathigounder Balasubramaniam,et al.  Chaotic synchronization of Rikitake system based on T-S fuzzy control techniques , 2013 .

[133]  Fetah Kolonić,et al.  Tensor Product Model Transformation-based Controller Design for Gantry Crane Control System – An Application Approach , 2006 .

[134]  Zhangming Peng,et al.  The application of data mining for marine diesel engine fault detection , 2012, FSKD.

[135]  A Kusiak,et al.  A Data-Driven Approach for Monitoring Blade Pitch Faults in Wind Turbines , 2011, IEEE Transactions on Sustainable Energy.

[136]  Péter Baranyi,et al.  TP model transformation as a way to LMI-based controller design , 2004, IEEE Transactions on Industrial Electronics.

[137]  Andrew Kusiak,et al.  Fault Monitoring of Wind Turbine Generator Brushes: A Data-Mining Approach , 2012 .

[138]  Walmir M. Caminhas,et al.  Adaptive fault detection and diagnosis using an evolving fuzzy classifier , 2013, Inf. Sci..

[139]  João Miguel da Costa Sousa,et al.  Application of evolving fuzzy modeling to fault tolerant control , 2010, Evol. Syst..

[140]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[141]  Radu-Emil Precup,et al.  Performance analysis of torque motor systems with PID controllers tuned by Bacterial Foraging Optimization algorithms , 2014, 2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA).

[142]  Plamen P. Angelov,et al.  A new type of simplified fuzzy rule-based system , 2012, Int. J. Gen. Syst..

[143]  Eric Duviella,et al.  Advanced Pattern Recognition Approach for Fault Diagnosis of Wind Turbines , 2013, 2013 12th International Conference on Machine Learning and Applications.

[144]  Latesh G. Malik,et al.  A review on real time data stream classification and adapting to various concept drift scenarios , 2014, 2014 IEEE International Advance Computing Conference (IACC).

[145]  Plamen P. Angelov,et al.  Fully unsupervised fault detection and identification based on recursive density estimation and self-evolving cloud-based classifier , 2015, Neurocomputing.

[146]  Han Jiguang,et al.  Wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree SVM , 2013 .

[147]  Stefan Preitl,et al.  Fuzzy Control Systems With Reduced Parametric Sensitivity Based on Simulated Annealing , 2012, IEEE Transactions on Industrial Electronics.

[148]  Radu-Emil Precup,et al.  Bacterial Foraging Optimization approach to the controller tuning for automotive torque motors , 2014, 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE).

[149]  Bo Xing,et al.  Gravitational Search Algorithm , 2014 .

[150]  A. Kunakorn,et al.  A novel fuzzy logic control technique tuned by particle swarm optimization for maximum power point tracking for a photovoltaic system using a current-mode boost converter with bifurcation control , 2010 .

[151]  Sergio M. Savaresi,et al.  Optimal input design for direct data-driven tuning of model-reference controllers , 2013, Autom..

[152]  Shanlin Yang,et al.  Application of an effective modified gravitational search algorithm for the coordinated scheduling problem in a two-stage supply chain , 2014 .

[153]  Stefan Preitl,et al.  Three Evolutionary Optimization Algorithms in PI Controller Tuning , 2012 .

[154]  Yue Zhao,et al.  Wind turbine fault detection and isolation using support vector machine and a residual-based method , 2013, 2013 American Control Conference.

[155]  Oscar Castillo,et al.  A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation , 2014, Expert Syst. Appl..

[156]  Stefan Preitl,et al.  Application of IFT and SPSA to Servo System Control , 2011, IEEE Transactions on Neural Networks.

[157]  Nasser Sadati,et al.  Design of an H∞ PID controller using particle swarm optimization , 2009 .

[158]  John Dalsgaard Sørensen,et al.  Physics of failure as a basis for solder elements reliability assessment in wind turbines , 2012, Reliab. Eng. Syst. Saf..

[159]  M. Araki,et al.  Two-Degree-of-Freedom PID Controllers , 2003 .

[160]  Peter Baranyi,et al.  Tensor-product model-based control of two-dimensional aeroelastic system , 2006 .

[161]  Stefan Preitl,et al.  PI and PID controllers tuning for integral-type servo systems to ensure robust stability and controller robustness , 2006 .

[162]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[163]  Boukhemis Chetate,et al.  Artificial neural network control of the recycle compression system , 2014 .

[164]  Zhixin Yang,et al.  Real-time fault diagnosis for gas turbine generator systems using extreme learning machine , 2014, Neurocomputing.

[165]  Jingjing Liu,et al.  Estimation of an incipient fault using an adaptive neurofuzzy sliding-mode observer , 2014 .

[166]  Stefan Preitl,et al.  Fuzzy Controllers With Maximum Sensitivity for Servosystems , 2007, IEEE Transactions on Industrial Electronics.

[167]  Silvio Simani,et al.  Hybrid Model–Based Fault Detection of Wind Turbine Sensors , 2011 .

[168]  Frank L. Lewis,et al.  Intelligent Fault Diagnosis and Prognosis for Engineering Systems , 2006 .

[169]  Peter Galambos,et al.  Representing the model of impedance controlled robot interaction with feedback delay in polytopic LPV form: TP model transformation based approach , 2013 .

[170]  Ajith Abraham,et al.  Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications , 2009, Foundations of Computational Intelligence.

[171]  Peter Baranyi,et al.  Aeroelastic wing section control via relaxed tensor product model transformation framework , 2014 .

[172]  Plamen Angelov,et al.  Real-Time Fault Detection Using Recursive Density Estimation , 2014, Journal of Control, Automation and Electrical Systems.

[173]  Mounir Ayadi,et al.  PID-type fuzzy logic controller tuning based on particle swarm optimization , 2012, Eng. Appl. Artif. Intell..

[174]  B. Schutz.  Gravity from the ground up , 2003 .

[175]  Imtiaz Hussain Khan.  A Comparative Study of Evolutionary Algorithms , 2014 .

[176]  Yeung Yam,et al.  From differential equations to PDC controller design via numerical transformation , 2003, Comput. Ind..

[177]  Andrew Kusiak,et al.  The prediction and diagnosis of wind turbine faults , 2011 .

[178]  Cédric Join,et al.  Model-free control , 2013, Int. J. Control.