Research developments in adaptive intelligent vibration control of smart civil structures

Control algorithms are the most critical aspects in the successful control of civil structures subjected to earthquake and wind forces. In recent years, adaptive intelligent control algorithms are emerging as an acceptable substitute method to conventional model-based control algorithms. These algorithms mainly work on the principles of artificial intelligence (AI) and soft computing (SC) methods that make them highly efficient in controlling highly nonlinear, time-varying, and time-delayed complex civil structures. The current research probes to control algorithms, that this article set forth an inclusive state-of-the-art review of adaptive intelligent control (AIC) algorithms for vibration control of smart civil structures. First, a general introduction to adaptive intelligent control is presented along with its advantages over conventional control algorithms. Second, their classification concerning artificial intelligence and soft computing methods is provided that mainly consists of artificial neural network-based controller, brain emotional learning-based intelligent controller, replicator dynamics-based controller, multi-agent system-based controller, support vector machine-based controller, fuzzy logic control, adaptive neuro-fuzzy inference system-based controller, adaptive filters-base controller, and meta-heuristic algorithms-based hybrid controllers. Third, a brief review of these algorithms with their developments on the theory and applications is provided. Fourth, we demonstrate a summarized overview of the cited literature with a brief trend analysis is presented. Finally, this study presents an overview of these innovative AIC methods that can demonstrate future directions. The contribution of this article is the anticipation of detailed and in-depth discussion into the perspective of AI and SC-based AIC method advances that enabled practical applications in attenuating vibration response of smart civil structures. Moreover, the review demonstrates the computing advantages of AIC over conventional controllers that are important in creating the next generation of smart civil structures.

[1]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[2]  Hao Yu,et al.  Selection of Proper Neural Network Sizes and Architectures—A Comparative Study , 2012, IEEE Transactions on Industrial Informatics.

[3]  Bijan Samali,et al.  Experimental study of semi-active magnetorheological elastomer base isolation system using optimal neuro fuzzy logic control , 2019, Mechanical Systems and Signal Processing.

[4]  Mohamed Medhat Gaber,et al.  RED-GENE: An Evolutionary Game Theoretic Approach to Adaptive Data Stream Classification , 2019, IEEE Access.

[5]  Hojjat Adeli,et al.  Semi-active vibration control of smart isolated highway bridge structures using replicator dynamics , 2019, Engineering Structures.

[6]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[7]  Hongwei Mo,et al.  Nonlinear and Adaptive Intelligent Control Techniques for Quadrotor UAV – A Survey , 2019 .

[8]  Clarence W. de Silva,et al.  Intelligent Control: Fuzzy Logic Applications , 1995 .

[9]  Bart De Schutter,et al.  Multi-agent Reinforcement Learning: An Overview , 2010 .

[10]  Amin Hosseini,et al.  Online self‐tuning mechanism for direct adaptive control of tall building , 2018 .

[11]  Qing-Long Han,et al.  Recent advances in vibration control of offshore platforms , 2017 .

[12]  Xin-She Yang,et al.  Optimization in Civil Engineering and Metaheuristic Algorithms: A Review of State-of-the-Art Developments , 2018, Computational Intelligence, Optimization and Inverse Problems with Applications in Engineering.

[13]  Luciana R. Barroso,et al.  Simple adaptive control method for mitigating the seismic responses of coupled adjacent buildings considering parameter variations , 2019, Engineering Structures.

[14]  Saeed Sharifian,et al.  A modified water cycle evolutionary game theory algorithm to utilize QoS for IoT services in cloud-assisted fog computing environments , 2019, The Journal of Supercomputing.

[15]  K. Sivakumar,et al.  Adaptive Neuro Fuzzy Inference System control of active suspension system with actuator dynamics , 2018 .

[16]  Ramin Rajabioun,et al.  Cuckoo Optimization Algorithm , 2011, Appl. Soft Comput..

[17]  Luis Lara,et al.  Structural control strategies based on magnetorheological dampers managed using artificial neural networks and fuzzy logic , 2017 .

[18]  Bogdan Sapiński,et al.  Analysis of parametric models of MR linear damper , 2003 .

[19]  Lipo Wang Support vector machines : theory and applications , 2005 .

[20]  Seyed Mehdi Zahrai,et al.  Semi-active seismic control of an 11-DOF building model with TMD+MR damper using type-1 and -2 fuzzy algorithms , 2018 .

[21]  Wei-Ho Tsai,et al.  Improving search engine optimization (SEO) by using hybrid modified MCDM models , 2018, Artificial Intelligence Review.

[22]  P. Taylor,et al.  Evolutionarily Stable Strategies and Game Dynamics , 1978 .

[23]  Kenneth V. Price,et al.  Differential Evolution: A Practical Approach to Global Optimization , 2014 .

[24]  Peter Xiaoping Liu,et al.  Adaptive Intelligent Control of Nonaffine Nonlinear Time-Delay Systems With Dynamic Uncertainties , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[25]  Salem Alkhalaf,et al.  Nature-inspired algorithms for feed-forward neural network classifiers: A survey of one decade of research , 2020 .

[26]  J. Weibull,et al.  Nash Equilibrium and Evolution by Imitation , 1994 .

[27]  E. H. Mamdani,et al.  Learning Control Algorithms in Real Dynamic Systems , 1974 .

[28]  Vincenzo Capasso,et al.  Multiscale Problems in the Life Sciences , 2008 .

[29]  K. Thiele,et al.  Tuned mass dampers in wind response control of wind turbine with soil-structure interaction , 2020 .

[30]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[31]  Surendra Kumar,et al.  Modeling of a neural network based controller for vibration suppression of a building structure , 2018 .

[32]  Luciana R. Barroso,et al.  Adaptive neuro-fuzzy and simple adaptive control methods for full three-dimensional coupled buildings subjected to bi-directional seismic excitations , 2021 .

[33]  Ferruccio Resta,et al.  An adaptive non-model-based control strategy for smart structures vibration suppression , 2013, Smart Structures.

[34]  Bin He,et al.  Optimising intelligent control of a highway bridge with magnetorheological dampers , 2020 .

[35]  Georgios E. Stavroulakis,et al.  Fuzzy and Neuro-fuzzy Control for Smart Structures , 2019, Computational Intelligence and Optimization Methods for Control Engineering.

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

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

[38]  Magdi S. Mahmoud,et al.  Adaptive intelligent techniques for microgrid control systems: A survey , 2017 .

[39]  R. Rupakhety,et al.  Analysis of a Benchmark Building Installed with Tuned Mass Dampers under Wind and Earthquake Loads , 2019 .

[40]  Mehmet Zile Intelligent and Adaptive Control , 2020 .

[41]  Simon Parsons,et al.  What evolutionary game theory tells us about multiagent learning , 2007, Artif. Intell..

[42]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[43]  Hojjat Adeli,et al.  Many-objective control optimization of high-rise building structures using replicator dynamics and neural dynamics model , 2017, Structural and Multidisciplinary Optimization.

[44]  Zainah Ibrahim,et al.  Invited Review: Recent developments in vibration control of building and bridge structures , 2017 .

[45]  Madan Gopal,et al.  SVM-Based Tree-Type Neural Networks as a Critic in Adaptive Critic Designs for Control , 2007, IEEE Transactions on Neural Networks.

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

[47]  Ilaria Venanzi,et al.  A Review on Adaptive Methods for Structural Control , 2016 .

[48]  Hyun-Su Kim,et al.  Semi-active fuzzy control of a wind-excited tall building using multi-objective genetic algorithm , 2012 .

[49]  Hongjin Kim,et al.  HYBRID FEEDBACK-LEAST MEAN SQUARE ALGORITHM FOR STRUCTURAL CONTROL , 2004 .

[50]  A. E. Taha,et al.  Vibration Control of a Tall Benchmark Building under Wind and Earthquake Excitation , 2021 .

[51]  Vojislav Kecman,et al.  Support Vector Machines – An Introduction , 2005 .

[52]  Hojjat Adeli,et al.  Vibration control of smart base-isolated irregular buildings using neural dynamic optimization model and replicator dynamics , 2018 .

[53]  Tilman Börgers,et al.  Learning Through Reinforcement and Replicator Dynamics , 1997 .

[54]  Hojjat Adeli,et al.  Optimization of space structures by neural dynamics , 1995, Neural Networks.

[55]  A. Preumont Vibration Control of Active Structures , 1997 .

[56]  Bernard Widrow,et al.  Adaptive Inverse Control Based on Nonlinear Adaptive Filtering , 1998 .

[57]  Jamshid Ghaboussi,et al.  Active Control of Structures Using Neural Networks , 1995 .

[58]  Luciana R. Barroso,et al.  Semi-active adaptive control for enhancing the seismic performance of nonlinear coupled buildings with smooth hysteretic behavior , 2019, Engineering Structures.

[59]  S. Andrew Gadsden,et al.  Gaussian filters for parameter and state estimation: A general review of theory and recent trends , 2017, Signal Process..

[60]  Carmine Lima,et al.  Soft computing techniques in structural and earthquake engineering: a literature review , 2020 .

[61]  Roque Alfredo Osornio-Rios,et al.  Vibration Control on Smart Civil Structures: A Review , 2014 .

[62]  Peter J. Gawthrop,et al.  Neural networks for control systems - A survey , 1992, Autom..

[63]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[64]  King-Sun Fu,et al.  Learning control systems and intelligent control systems: An intersection of artifical intelligence and automatic control , 1971 .

[65]  Munther A. Dahleh,et al.  Advancing systems and control research in the era of ML and AI , 2018, Annu. Rev. Control..

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

[67]  Xianxia Zhang,et al.  Online Support Vector Machine: A Survey , 2015, ICHSA.

[68]  Hui Gao,et al.  Nonfragile Finite-Time Extended Dissipative Control for a Class of Uncertain Switched Neutral Systems , 2017, Complex..

[69]  T. T. Soong,et al.  STRUCTURAL CONTROL: PAST, PRESENT, AND FUTURE , 1997 .

[70]  Colin B. Brown,et al.  Fuzzy Sets and Structural Engineering , 1983 .

[71]  J M Smith,et al.  Evolution and the theory of games , 1976 .

[72]  Bart De Schutter,et al.  A Comprehensive Survey of Multiagent Reinforcement Learning , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[73]  Nikhil Angad Bakshi Model Reference Adaptive Control of Quadrotor UAVs: A Neural Network Perspective , 2018 .

[74]  Shirley J. Dyke,et al.  Benchmark Control Problems for Seismically Excited Nonlinear Buildings , 2004 .

[75]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[76]  J. Love,et al.  Monitoring of a Tall Building Equipped with an Efficient Multiple-Tuned Sloshing Damper System , 2020 .

[77]  Keyhan Faraji Seismic performance of a semi-active MR damper improved by fuzzy control system , 2018 .

[78]  Hojjat Adeli,et al.  Intelligent Infrastructure: Neural Networks, Wavelets, and Chaos Theory for Intelligent Transportation Systems and Smart Structures , 2008 .

[79]  G. Wittum,et al.  Adaptive filtering , 1997 .

[80]  Jimi Tjong,et al.  Artificial neural network training utilizing the smooth variable structure filter estimation strategy , 2016, Neural Computing and Applications.

[81]  Xin-She Yang,et al.  Cuckoo search: recent advances and applications , 2013, Neural Computing and Applications.

[82]  Pandian Vasant,et al.  Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics , 2016 .

[83]  Lotfi A. Zadeh,et al.  Similarity relations and fuzzy orderings , 1971, Inf. Sci..

[84]  Xin-She Yang,et al.  Multiobjective cuckoo search for design optimization , 2013, Comput. Oper. Res..

[85]  Karl Tuyls,et al.  Evolutionary Dynamics of Multi-Agent Learning: A Survey , 2015, J. Artif. Intell. Res..

[86]  Hyung-Jo Jung,et al.  Application of some semi‐active control algorithms to a smart base‐isolated building employing MR dampers , 2006 .

[87]  Ehsan Noroozinejad Farsangi,et al.  Seismic Performance Evaluation of a Recently Developed Magnetorheological Damper: Experimental Investigation , 2021 .

[88]  A. Jain,et al.  Seismic response control of base-isolated buildings using tuned mass damper , 2020, Australian Journal of Structural Engineering.

[89]  Ke Chang,et al.  Performance of a nonlinear hybrid base isolation system under the ground motions , 2021 .

[90]  A. Rama Mohan Rao,et al.  Multi-objective optimal design of fuzzy logic controller using a self configurable swarm intelligence algorithm , 2008 .

[91]  M. Aldawod,et al.  Active control of wind excited structures using fuzzy logic , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[92]  A. K-Karamodin,et al.  Semi‐active control of structures using neuro‐predictive algorithm for MR dampers , 2008 .

[93]  Luciana R. Barroso,et al.  Response attenuation of cable-stayed bridge subjected to central US earthquakes using neuro-fuzzy and simple adaptive control , 2020 .

[94]  Zubaidah Ismail,et al.  RECENT DEVELOPMENTS IN DAMAGE IDENTIFICATION OF STRUCTURES USING DATA MINING , 2017 .

[95]  L. Zadeh Probability measures of Fuzzy events , 1968 .

[96]  Ilya Pavlyukevich Lévy flights, non-local search and simulated annealing , 2007, J. Comput. Phys..

[97]  Fereidoun Amini,et al.  Neural Network for Structure Control , 1995 .

[98]  Hadi Salehi,et al.  Emerging artificial intelligence methods in structural engineering , 2018, Engineering Structures.

[99]  Peter J. Fleming,et al.  Evolutionary algorithms in control systems engineering: a survey , 2002 .

[100]  Mouhacine Benosman,et al.  Model‐based vs data‐driven adaptive control: An overview , 2018 .

[101]  Wen Yu,et al.  Advances in modeling and vibration control of building structures , 2013, Annu. Rev. Control..

[102]  Touraj Taghikhany,et al.  Robust semi-active control for uncertain structures and smart dampers , 2014 .

[103]  Hyun-Su Kim,et al.  Semi-active Outrigger Damping System for Seismic Protection of Building Structure , 2017 .

[104]  Saeed Tavakoli,et al.  Adaptive fractional order fuzzy proportional–integral–derivative control of smart base-isolated structures equipped with magnetorheological dampers , 2017 .

[105]  Joshua B. Tenenbaum,et al.  Building machines that learn and think like people , 2016, Behavioral and Brain Sciences.

[106]  Aurelio Uncini Fundamentals of Adaptive Signal Processing , 2014 .

[107]  Mohammad Reza Khalghani,et al.  An optimal and intelligent control strategy for a class of nonlinear systems: adaptive fuzzy sliding mode , 2016 .

[108]  Ángel Sánchez,et al.  Evolutionary game theory: Temporal and spatial effects beyond replicator dynamics , 2009, Physics of life reviews.

[109]  Hojjat Adeli,et al.  Advances in optimization of highrise building structures , 2014 .

[110]  Zhuo Wang,et al.  From model-based control to data-driven control: Survey, classification and perspective , 2013, Inf. Sci..

[111]  Hyun-Su Kim,et al.  Design of fuzzy logic controller for smart base isolation system using genetic algorithm , 2006 .

[112]  Hyun-Su Kim,et al.  GA-fuzzy control of smart base isolated benchmark building using supervisory control technique , 2007, Adv. Eng. Softw..

[113]  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.

[114]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[115]  Hong-Nan Li,et al.  Self-Tuning Fuzzy Control for Seismic Protection of Smart Base-Isolated Buildings Subjected to Pulse-Type Near-Fault Earthquakes , 2017 .

[116]  Rogelio Lozano,et al.  Introduction to Adaptive Control , 2011 .

[117]  Lucia Faravelli,et al.  Use of Adaptive Networks in Fuzzy Control of Civil Structures , 1996 .

[118]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[119]  Hojjat Adeli,et al.  Recent advances in control algorithms for smart structures and machines , 2017, Expert Syst. J. Knowl. Eng..

[120]  Hojjat Adeli,et al.  Multi-agent replicator controller for sustainable vibration control of smart structures , 2017 .

[121]  Pierre Roussel-Ragot,et al.  Neural Networks and Nonlinear Adaptive Filtering: Unifying Concepts and New Algorithms , 1993, Neural Computation.

[122]  Geuntaek Kang,et al.  Adaptive–intelligent control by neural‐net systems , 1998 .

[123]  Xiaodong Li,et al.  Swarm Intelligence in Optimization , 2008, Swarm Intelligence.

[124]  B.M. Wilamowski,et al.  Neural network architectures and learning algorithms , 2009, IEEE Industrial Electronics Magazine.

[125]  Ann Nowé,et al.  Evolutionary game theory and multi-agent reinforcement learning , 2005, The Knowledge Engineering Review.

[126]  Caro Lucas,et al.  Introducing Belbic: Brain Emotional Learning Based Intelligent Controller , 2004, Intell. Autom. Soft Comput..

[127]  Zbigniew Michalewicz,et al.  Variants of Evolutionary Algorithms for Real-World Applications , 2011, Variants of Evolutionary Algorithms for Real-World Applications.

[128]  Long Cheng,et al.  Decentralized Robust Adaptive Control for the Multiagent System Consensus Problem Using Neural Networks , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[129]  Seyed Mehdi Zahrai,et al.  Semi-active seismic control of buildings using MR damper and adaptive neural-fuzzy intelligent controller optimized with genetic algorithm , 2019 .

[130]  Zhihong Man,et al.  Lyapunov-theory-based radial basis function networks for adaptive filtering , 2002 .

[131]  J. Nash Equilibrium Points in N-Person Games. , 1950, Proceedings of the National Academy of Sciences of the United States of America.

[132]  Masoud Zabihi-Samani,et al.  Optimal Semi-active Structural Control with a Wavelet-Based Cuckoo-Search Fuzzy Logic Controller , 2018 .

[133]  Charu C. Aggarwal,et al.  Neural Networks and Deep Learning , 2018, Springer International Publishing.

[134]  Manuel Laguna,et al.  Tabu Search , 1997 .

[135]  Marc Peter Deisenroth,et al.  Deep Reinforcement Learning: A Brief Survey , 2017, IEEE Signal Processing Magazine.

[136]  L. P. Holmblad,et al.  CONTROL OF A CEMENT KILN BY FUZZY LOGIC , 1993 .

[137]  Qing Liu,et al.  Support vector machine based semi‐active control of structures: a new control strategy , 2011 .

[138]  Vasant Matsagar,et al.  Research developments in vibration control of structures using passive tuned mass dampers , 2017, Annu. Rev. Control..

[139]  Seongkyu Chang,et al.  Modal-Energy-Based Neuro-Controller for Seismic Response Reduction of a Nonlinear Building Structure , 2019, Applied Sciences.

[140]  Ali Chaibakhsh,et al.  Semiactive conceptual fuzzy control of magnetorheological dampers in an irregular base‐isolated benchmark building optimized by multi‐objective genetic algorithm , 2018, Structural Control and Health Monitoring.