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    A review of mobile robots: Concepts, methods, theoretical framework, and applications

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    [EN] Humanoid robots, unmanned rovers, entertainment pets, drones, and so on are great examples of mobile robots. They can be distinguished from other robots by their ability to move autonomously, with enough intelligence to react and make decisions based on the perception they receive from the environment. Mobile robots must have some source of input data, some way of decoding that input, and a way of taking actions (including its own motion) to respond to a changing world. The need to sense and adapt to an unknown environment requires a powerful cognition system. Nowadays, there are mobile robots that can walk, run, jump, and so on like their biological counterparts. Several fields of robotics have arisen, such as wheeled mobile robots, legged robots, flying robots, robot vision, artificial intelligence, and so on, which involve different technological areas such as mechanics, electronics, and computer science. In this article, the world of mobile robots is explored including the new trends. These new trends are led by artificial intelligence, autonomous driving, network communication, cooperative work, nanorobotics, friendly human-robot interfaces, safe human-robot interaction, and emotion expression and perception. Furthermore, these news trends are applied to different fields such as medicine, health care, sports, ergonomics, industry, distribution of goods, and service robotics. These tendencies will keep going their evolution in the coming years.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Spanish Ministry of Economy and Competitiveness, which has funded the DPI2013-44227-R project.Rubio Montoya, FJ.; Valero Chuliá, FJ.; Llopis Albert, C. (2019). A review of mobile robots: Concepts, methods, theoretical framework, and applications. International Journal of Advanced Robotic Systems. 16(2):1-22. https://doi.org/10.1177/1729881419839596S122162Brunete, A., Ranganath, A., Segovia, S., de Frutos, J. P., Hernando, M., & Gambao, E. (2017). Current trends in reconfigurable modular robots design. International Journal of Advanced Robotic Systems, 14(3), 172988141771045. doi:10.1177/1729881417710457Bajracharya, M., Maimone, M. W., & Helmick, D. (2008). Autonomy for Mars Rovers: Past, Present, and Future. Computer, 41(12), 44-50. doi:10.1109/mc.2008.479Carsten, J., Rankin, A., Ferguson, D., & Stentz, A. (2007). Global Path Planning on Board the Mars Exploration Rovers. 2007 IEEE Aerospace Conference. doi:10.1109/aero.2007.352683Grotzinger, J. P., Crisp, J., Vasavada, A. R., Anderson, R. C., Baker, C. J., Barry, R., … Wiens, R. C. (2012). Mars Science Laboratory Mission and Science Investigation. Space Science Reviews, 170(1-4), 5-56. doi:10.1007/s11214-012-9892-2Khatib, O., Yeh, X., Brantner, G., Soe, B., Kim, B., Ganguly, S., … Creuze, V. (2016). Ocean One: A Robotic Avatar for Oceanic Discovery. IEEE Robotics & Automation Magazine, 23(4), 20-29. doi:10.1109/mra.2016.2613281Ceccarelli, M. (2012). Notes for a History of Grasping Devices. Mechanisms and Machine Science, 3-16. doi:10.1007/978-1-4471-4664-3_1Campion, G., & Chung, W. (2008). Wheeled Robots. Springer Handbook of Robotics, 391-410. doi:10.1007/978-3-540-30301-5_18Ferriere, L., Raucent, B., & Campion, G. (s. f.). Design of omnimobile robot wheels. Proceedings of IEEE International Conference on Robotics and Automation. doi:10.1109/robot.1996.509271Campion, G., Bastin, G., & Dandrea-Novel, B. (1996). Structural properties and classification of kinematic and dynamic models of wheeled mobile robots. IEEE Transactions on Robotics and Automation, 12(1), 47-62. doi:10.1109/70.481750Bałchanowski, J. (2012). Mobile Wheel-Legged Robot: Researching of Suspension Leveling System. Mechanisms and Machine Science, 3-12. doi:10.1007/978-94-007-5125-5_1Williams, R. L., Carter, B. E., Gallina, P., & Rosati, G. (2002). Dynamic model with slip for wheeled omnidirectional robots. IEEE Transactions on Robotics and Automation, 18(3), 285-293. doi:10.1109/tra.2002.1019459Chan, R. P. M., Stol, K. A., & Halkyard, C. R. (2013). Review of modelling and control of two-wheeled robots. Annual Reviews in Control, 37(1), 89-103. doi:10.1016/j.arcontrol.2013.03.004Kim, H., & Kim, B. K. (2014). Online Minimum-Energy Trajectory Planning and Control on a Straight-Line Path for Three-Wheeled Omnidirectional Mobile Robots. IEEE Transactions on Industrial Electronics, 61(9), 4771-4779. doi:10.1109/tie.2013.2293706Carbone, G., & Ceccarelli, M. (2005). Legged Robotic Systems. Cutting Edge Robotics. doi:10.5772/4669Chestnutt, J., Lau, M., Cheung, G., Kuffner, J., Hodgins, J., & Kanade, T. (s. f.). Footstep Planning for the Honda ASIMO Humanoid. Proceedings of the 2005 IEEE International Conference on Robotics and Automation. doi:10.1109/robot.2005.1570188Arikawa, K., & Hirose, S. (s. f.). Development of quadruped walking robot TITAN-VIII. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS ’96. doi:10.1109/iros.1996.570670Kurazume, R., Byong-won, A., Ohta, K., & Hasegawa, T. (s. f.). Experimental study on energy efficiency for quadruped walking vehicles. Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453). doi:10.1109/iros.2003.1250697Hirose, S., Fukuda, Y., Yoneda, K., Nagakubo, A., Tsukagoshi, H., Arikawa, K., … Hodoshima, R. (2009). Quadruped walking robots at Tokyo Institute of Technology. IEEE Robotics & Automation Magazine, 16(2), 104-114. doi:10.1109/mra.2009.932524Stoica, A., Carbone, G., Ceccarelli, M., & Pisla, D. (2010). Cassino Hexapod : Experiences and new leg design. 2010 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR). doi:10.1109/aqtr.2010.5520756Bares, J. E., & Wettergreen, D. S. (1999). Dante II: Technical Description, Results, and Lessons Learned. The International Journal of Robotics Research, 18(7), 621-649. doi:10.1177/02783649922066475Schiele, A., Romstedt, J., Lee, C., Henkel, H., Klinkner, S., Bertrand, R., … Michaelis, H. (2008). NanoKhod Exploration Rover - A Rugged Rover Suited for Small, Low-Cost, Planetary Lander Mission. IEEE Robotics & Automation Magazine, 15(2), 96-107. doi:10.1109/mra.2008.917888Takayama, T., & Hirose, S. (2003). Development of Souryu I & II -Connected Crawler Vehicle for Inspection of Narrow and Winding Space. Journal of Robotics and Mechatronics, 15(1), 61-69. doi:10.20965/jrm.2003.p0061Cuesta, F., & Ollero, A. (2005). Intelligent Mobile Robot Navigation. Springer Tracts in Advanced Robotics. doi:10.1007/b14079Ohya, I., Kosaka, A., & Kak, A. (1998). Vision-based navigation by a mobile robot with obstacle avoidance using single-camera vision and ultrasonic sensing. IEEE Transactions on Robotics and Automation, 14(6), 969-978. doi:10.1109/70.736780Desouza, G. N., & Kak, A. C. (2002). Vision for mobile robot navigation: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(2), 237-267. doi:10.1109/34.982903Borenstein, J., Everett, H. R., Feng, L., & Wehe, D. (1997). Mobile robot positioning: Sensors and techniques. Journal of Robotic Systems, 14(4), 231-249. doi:10.1002/(sici)1097-4563(199704)14:43.0.co;2-rBetke, M., & Gurvits, L. (1997). Mobile robot localization using landmarks. IEEE Transactions on Robotics and Automation, 13(2), 251-263. doi:10.1109/70.563647Kuffner, J., Nishiwaki, K., Kagami, S., Inaba, M., & Inoue, H. (2005). Motion Planning for Humanoid Robots. Robotics Research. The Eleventh International Symposium, 365-374. doi:10.1007/11008941_39Lee, Y.-J., & Bien, Z. (2002). Path planning for a quadruped robot: an artificial field approach. Advanced Robotics, 16(7), 609-627. doi:10.1163/15685530260390746Petres, C., Pailhas, Y., Patron, P., Petillot, Y., Evans, J., & Lane, D. (2007). Path Planning for Autonomous Underwater Vehicles. IEEE Transactions on Robotics, 23(2), 331-341. doi:10.1109/tro.2007.895057P. Raja. (2012). Optimal path planning of mobile robots: A review. International Journal of the Physical Sciences, 7(9). doi:10.5897/ijps11.1745Hart, P., Nilsson, N., & Raphael, B. (1968). A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), 100-107. doi:10.1109/tssc.1968.300136Lozano-Pérez, T., & Wesley, M. A. (1979). An algorithm for planning collision-free paths among polyhedral obstacles. Communications of the ACM, 22(10), 560-570. doi:10.1145/359156.359164Lozano-Perez. (1983). Spatial Planning: A Configuration Space Approach. IEEE Transactions on Computers, C-32(2), 108-120. doi:10.1109/tc.1983.1676196Brooks, R. A. (1983). Solving the find-path problem by good representation of free space. IEEE Transactions on Systems, Man, and Cybernetics, SMC-13(2), 190-197. doi:10.1109/tsmc.1983.6313112Schwartz, J. T., & Sharir, M. (1983). On the «piano movers» problem. II. General techniques for computing topological properties of real algebraic manifolds. Advances in Applied Mathematics, 4(3), 298-351. doi:10.1016/0196-8858(83)90014-3Kavraki LE. Random networks in configurations space for fast path planning. Doctoral dissertation, Department of Computer Science, Stanford University, Stanford, CA, 1994.Kavraki, L. E., Latombe, J.-C., Motwani, R., & Raghavan, P. (1998). Randomized Query Processing in Robot Path Planning. Journal of Computer and System Sciences, 57(1), 50-60. doi:10.1006/jcss.1998.1578Hsu, D., Kindel, R., Latombe, J.-C., & Rock, S. (2002). Randomized Kinodynamic Motion Planning with Moving Obstacles. The International Journal of Robotics Research, 21(3), 233-255. doi:10.1177/027836402320556421Kavraki, L. E., Svestka, P., Latombe, J.-C., & Overmars, M. H. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4), 566-580. doi:10.1109/70.508439Rubio, F., Valero, F., Sunyer, J., & Mata, V. (2009). Direct step‐by‐step method for industrial robot path planning. Industrial Robot: An International Journal, 36(6), 594-607. doi:10.1108/01439910910994669Howard, T. M., & Kelly, A. (2007). Optimal Rough Terrain Trajectory Generation for Wheeled Mobile Robots. The International Journal of Robotics Research, 26(2), 141-166. doi:10.1177/0278364906075328Valero FJ. Planificación de trayectorias libres de obstáculos para un manipulador plano. Doctoral Thesis, UPV, Spain, 1990.Valero, F., Mata, V., Cuadrado, J. I., & Ceccarelli, M. (1996). A formulation for path planning of manipulators in complex environments by using adjacent configurations. Advanced Robotics, 11(1), 33-56. doi:10.1163/156855397x00038Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. doi:10.1109/4235.996017Garcia, M. A. P., Montiel, O., Castillo, O., Sepúlveda, R., & Melin, P. (2009). Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation. Applied Soft Computing, 9(3), 1102-1110. doi:10.1016/j.asoc.2009.02.014Miao, H., & Tian, Y.-C. (2013). Dynamic robot path planning using an enhanced simulated annealing approach. Applied Mathematics and Computation, 222, 420-437. doi:10.1016/j.amc.2013.07.022Bobrow, J. E., Dubowsky, S., & Gibson, J. S. (1985). Time-Optimal Control of Robotic Manipulators Along Specified Paths. The International Journal of Robotics Research, 4(3), 3-17. doi:10.1177/027836498500400301Kang Shin, & McKay, N. (1985). Minimum-time control of robotic manipulators with geometric path constraints. IEEE Transactions on Automatic Control, 30(6), 531-541. doi:10.1109/tac.1985.1104009Kyriakopoulos, K. J., & Saridis, G. N. (s. f.). Minimum jerk path generation. Proceedings. 1988 IEEE International Conference on Robotics and Automation. doi:10.1109/robot.1988.12075Constantinescu, D., & Croft, E. A. (2000). Smooth and time-optimal trajectory planning for industrial manipulators along specified paths. Journal of Robotic Systems, 17(5), 233-249. doi:10.1002/(sici)1097-4563(200005)17:53.0.co;2-yGasparetto, A., & Zanotto, V. (2010). Optimal trajectory planning for industrial robots. Advances in Engineering Software, 41(4), 548-556. doi:10.1016/j.advengsoft.2009.11.001JIANGdagger, Z.-P., & NIJMEIJER, H. (1997). Tracking Control of Mobile Robots: A Case Study in Backstepping**This paper was not presented at any IFAC meeting. This paper was recommended for publication in revised form by Associate Editor Alberto Isidori under the direction of Editor Tamer Başar. Automatica, 33(7), 1393-1399. doi:10.1016/s0005-1098(97)00055-1Klosowski, J. T., Held, M., Mitchell, J. S. B., Sowizral, H., & Zikan, K. (1998). Efficient collision detection using bounding volume hierarchies of k-DOPs. IEEE Transactions on Visualization and Computer Graphics, 4(1), 21-36. doi:10.1109/2945.675649Mirtich B. V-Clip: fast and robust polyhedral collision detection. Technical Report TR97-05, Mitsubishi Electric Research Laboratory, 1997.Mohamed, E. F., El-Metwally, K., & Hanafy, A. R. (2011). An improved Tangent Bug method integrated with artificial potential field for multi-robot path planning. 2011 International Symposium on Innovations in Intelligent Systems and Applications. doi:10.1109/inista.2011.5946136Seder, M., & Petrovic, I. (2007). Dynamic window based approach to mobile robot motion control in the presence of moving obstacles. Proceedings 2007 IEEE International Conference on Robotics and Automation. doi:10.1109/robot.2007.363613Simmons, R. (s. f.). The curvature-velocity method for local obstacle avoidance. Proceedings of IEEE International Conference on Robotics and Automation. doi:10.1109/robot.1996.51102

    Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control

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    [EN] New upper limb prostheses controllers are continuously being proposed in the literature. However, most of the prostheses commonly used in the real world are based on very old basic controllers. One reason to explain this reluctance to change is the lack of robustness. Traditional controllers have been validated by many users and years, so the introduction of a new controller paradigm requires a lot of strong evidence of a robust behavior. In this work, we approach the robustness against donning/doffing and arm position for recently proposed linear filter adaptive controllers based on myoelectric signals. The adaptive approach allows to introduce some feedback in a natural way in real time in the human-machine collaboration, so it is not so sensitive to input signals changes due to donning/doffing and arm movements. The average completion rate and path efficiency obtained for eight able-bodied subjects donning/doffing five times in four days is 95.83% and 84.19%, respectively, and for four participants using different arm positions is 93.84% and 88.77%, with no statistically significant difference in the results obtained for the different conditions. All these characteristics make the adaptive linear regression a potential candidate for future real world prostheses controllers.This work is partially supported by Ministerio de Educacion, Cultura y Deporte (Spain) under grant FPU15/02870. The authors would like to thank Lucas Parra for the Myo device and Janne M. Hahne for discussions about the subject of the paper.Igual, C.; Camacho-García, A.; Bernabeu Soler, EJ.; Igual García, J. (2020). Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control. Applied Sciences. 10(8):1-19. https://doi.org/10.3390/app10082892S119108Esquenazi, A. (2004). Amputation rehabilitation and prosthetic restoration. From surgery to community reintegration. Disability and Rehabilitation, 26(14-15), 831-836. doi:10.1080/09638280410001708850Ziegler-Graham, K., MacKenzie, E. J., Ephraim, P. L., Travison, T. G., & Brookmeyer, R. (2008). Estimating the Prevalence of Limb Loss in the United States: 2005 to 2050. Archives of Physical Medicine and Rehabilitation, 89(3), 422-429. doi:10.1016/j.apmr.2007.11.005Igual, C., Pardo, L. A., Hahne, J., & Igual, J. M. (2019). Myoelectric Control for Upper Limb Prostheses. Electronics, 8(11), 1244. doi:10.3390/electronics8111244Biddiss, E., & Chau, T. (2007). Upper-Limb Prosthetics. American Journal of Physical Medicine & Rehabilitation, 86(12), 977-987. doi:10.1097/phm.0b013e3181587f6cBiddiss, E. A., & Chau, T. T. (2007). Upper limb prosthesis use and abandonment. Prosthetics & Orthotics International, 31(3), 236-257. doi:10.1080/03093640600994581Davidson, J. (2002). A survey of the satisfaction of upper limb amputees with their prostheses, their lifestyles, and their abilities. Journal of Hand Therapy, 15(1), 62-70. doi:10.1053/hanthe.2002.v15.01562Datta, D., Selvarajah, K., & Davey, N. (2004). Functional outcome of patients with proximal upper limb deficiency–acquired and congenital. Clinical Rehabilitation, 18(2), 172-177. doi:10.1191/0269215504cr716oaVujaklija, I., Farina, D., & Aszmann, O. (2016). New developments in prosthetic arm systems. Orthopedic Research and Reviews, Volume 8, 31-39. doi:10.2147/orr.s71468Scheme, E. J., Englehart, K. B., & Hudgins, B. S. (2011). Selective Classification for Improved Robustness of Myoelectric Control Under Nonideal Conditions. IEEE Transactions on Biomedical Engineering, 58(6), 1698-1705. doi:10.1109/tbme.2011.2113182Englehart, K., & Hudgins, B. (2003). A robust, real-time control scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 50(7), 848-854. doi:10.1109/tbme.2003.813539Parker, P., Englehart, K., & Hudgins, B. (2006). Myoelectric signal processing for control of powered limb prostheses. Journal of Electromyography and Kinesiology, 16(6), 541-548. doi:10.1016/j.jelekin.2006.08.006Fougner, A., Stavdahl, Ø., Kyberd, P. J., Losier, Y. G., & Parker, P. A. (2012). Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control—A Review. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(5), 663-677. doi:10.1109/tnsre.2012.2196711Resnik, L., Huang, H. (Helen), Winslow, A., Crouch, D. L., Zhang, F., & Wolk, N. (2018). Evaluation of EMG pattern recognition for upper limb prosthesis control: a case study in comparison with direct myoelectric control. Journal of NeuroEngineering and Rehabilitation, 15(1). doi:10.1186/s12984-018-0361-3Sartori, M., Durandau, G., Došen, S., & Farina, D. (2018). Robust simultaneous myoelectric control of multiple degrees of freedom in wrist-hand prostheses by real-time neuromusculoskeletal modeling. Journal of Neural Engineering, 15(6), 066026. doi:10.1088/1741-2552/aae26bVelliste, M., Perel, S., Spalding, M. C., Whitford, A. S., & Schwartz, A. B. (2008). Cortical control of a prosthetic arm for self-feeding. Nature, 453(7198), 1098-1101. doi:10.1038/nature06996Amsuess, S., Vujaklija, I., Goebel, P., Roche, A. D., Graimann, B., Aszmann, O. C., & Farina, D. (2016). Context-Dependent Upper Limb Prosthesis Control for Natural and Robust Use. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(7), 744-753. doi:10.1109/tnsre.2015.2454240Kuiken, T. A., Miller, L. A., Turner, K., & Hargrove, L. J. (2016). A Comparison of Pattern Recognition Control and Direct Control of a Multiple Degree-of-Freedom Transradial Prosthesis. IEEE Journal of Translational Engineering in Health and Medicine, 4, 1-8. doi:10.1109/jtehm.2016.2616123Phinyomark, A., N. Khushaba, R., & Scheme, E. (2018). Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors. Sensors, 18(5), 1615. doi:10.3390/s18051615Asghari Oskoei, M., & Hu, H. (2007). Myoelectric control systems—A survey. Biomedical Signal Processing and Control, 2(4), 275-294. doi:10.1016/j.bspc.2007.07.009Spanias, J. A., Perreault, E. J., & Hargrove, L. J. (2016). Detection of and Compensation for EMG Disturbances for Powered Lower Limb Prosthesis Control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(2), 226-234. doi:10.1109/tnsre.2015.2413393Castellini, C., & van der Smagt, P. (2008). Surface EMG in advanced hand prosthetics. Biological Cybernetics, 100(1), 35-47. doi:10.1007/s00422-008-0278-1Ameri, A., Akhaee, M. A., Scheme, E., & Englehart, K. (2019). Regression convolutional neural network for improved simultaneous EMG control. Journal of Neural Engineering, 16(3), 036015. doi:10.1088/1741-2552/ab0e2eHahne, J. M., Biebmann, F., Jiang, N., Rehbaum, H., Farina, D., Meinecke, F. C., … Parra, L. C. (2014). Linear and Nonlinear Regression Techniques for Simultaneous and Proportional Myoelectric Control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(2), 269-279. doi:10.1109/tnsre.2014.2305520Ameri, A., Scheme, E. J., Kamavuako, E. N., Englehart, K. B., & Parker, P. A. (2014). Real-Time, Simultaneous Myoelectric Control Using Force and Position-Based Training Paradigms. IEEE Transactions on Biomedical Engineering, 61(2), 279-287. doi:10.1109/tbme.2013.2281595Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan, A. H., … Donoghue, J. P. (2006). Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature, 442(7099), 164-171. doi:10.1038/nature04970Interface Prostheses With Classifier-Feedback-Based User Training. (2017). IEEE Transactions on Biomedical Engineering, 64(11), 2575-2583. doi:10.1109/tbme.2016.2641584Thomas, N., Ung, G., McGarvey, C., & Brown, J. D. (2019). Comparison of vibrotactile and joint-torque feedback in a myoelectric upper-limb prosthesis. Journal of NeuroEngineering and Rehabilitation, 16(1). doi:10.1186/s12984-019-0545-5Guémann, M., Bouvier, S., Halgand, C., Borrini, L., Paclet, F., Lapeyre, E., … de Rugy, A. (2018). Sensory and motor parameter estimation for elbow myoelectric control with vibrotactile feedback. Annals of Physical and Rehabilitation Medicine, 61, e467. doi:10.1016/j.rehab.2018.05.1090Markovic, M., Schweisfurth, M. A., Engels, L. F., Farina, D., & Dosen, S. (2018). Myocontrol is closed-loop control: incidental feedback is sufficient for scaling the prosthesis force in routine grasping. Journal of NeuroEngineering and Rehabilitation, 15(1). doi:10.1186/s12984-018-0422-7Pasquina, P. F., Perry, B. N., Miller, M. E., Ling, G. S. F., & Tsao, J. W. (2015). Recent advances in bioelectric prostheses. Neurology: Clinical Practice, 5(2), 164-170. doi:10.1212/cpj.0000000000000132NING, J., & Dario, F. (2014). Myoelectric control of upper limb prosthesis: current status, challenges and recent advances. Frontiers in Neuroengineering, 7. doi:10.3389/conf.fneng.2014.11.00004Lendaro, E., Mastinu, E., Håkansson, B., & Ortiz-Catalan, M. (2017). Real-time Classification of Non-Weight Bearing Lower-Limb Movements Using EMG to Facilitate Phantom Motor Execution: Engineering and Case Study Application on Phantom Limb Pain. Frontiers in Neurology, 8. doi:10.3389/fneur.2017.00470Mastinu, E., Ortiz-Catalan, M., & Hakansson, B. (2015). Analog front-ends comparison in the way of a portable, low-power and low-cost EMG controller based on pattern recognition. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi:10.1109/embc.2015.7318805BECK, T. W., HOUSH, T. J., CRAMER, J. T., MALEK, M. H., MIELKE, M., HENDRIX, R., & WEIR, J. P. (2008). Electrode Shift and Normalization Reduce the Innervation Zone’s Influence on EMG. Medicine & Science in Sports & Exercise, 40(7), 1314-1322. doi:10.1249/mss.0b013e31816c4822Pasquina, P. F., Evangelista, M., Carvalho, A. J., Lockhart, J., Griffin, S., Nanos, G., … Hankin, D. (2015). First-in-man demonstration of a fully implanted myoelectric sensors system to control an advanced electromechanical prosthetic hand. Journal of Neuroscience Methods, 244, 85-93. doi:10.1016/j.jneumeth.2014.07.016Fougner, A., Scheme, E., Chan, A. D. C., Englehart, K., & Stavdahl, Ø. (2011). Resolving the Limb Position Effect in Myoelectric Pattern Recognition. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19(6), 644-651. doi:10.1109/tnsre.2011.2163529Hwang, H.-J., Hahne, J. M., & Müller, K.-R. (2017). Real-time robustness evaluation of regression based myoelectric control against arm position change and donning/doffing. PLOS ONE, 12(11), e0186318. doi:10.1371/journal.pone.0186318Young, A. J., Hargrove, L. J., & Kuiken, T. A. (2011). The Effects of Electrode Size and Orientation on the Sensitivity of Myoelectric Pattern Recognition Systems to Electrode Shift. IEEE Transactions on Biomedical Engineering, 58(9), 2537-2544. doi:10.1109/tbme.2011.2159216Prahm, C., Schulz, A., Paaben, B., Schoisswohl, J., Kaniusas, E., Dorffner, G., … Aszmann, O. (2019). Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(5), 956-962. doi:10.1109/tnsre.2019.2907200Cipriani, C., Sassu, R., Controzzi, M., & Carrozza, M. C. (2011). Influence of the weight actions of the hand prosthesis on the performance of pattern recognition based myoelectric control: Preliminary study. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. doi:10.1109/iembs.2011.6090468Amsuss, S., Paredes, L. P., Rudigkeit, N., Graimann, B., Herrmann, M. J., & Farina, D. (2013). Long term stability of surface EMG pattern classification for prosthetic control. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi:10.1109/embc.2013.6610327Scheme, E., & Englehart, K. (2011). Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use. The Journal of Rehabilitation Research and Development, 48(6), 643. doi:10.1682/jrrd.2010.09.0177Scheme, E., Fougner, A., Stavdahl, Ø., Chan, A. D. C., & Englehart, K. (2010). Examining the adverse effects of limb position on pattern recognition based myoelectric control. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. doi:10.1109/iembs.2010.5627638Dohnalek, P., Gajdos, P., & Peterek, T. (2013). Human activity recognition on raw sensor data via sparse approximation. 2013 36th International Conference on Telecommunications and Signal Processing (TSP). doi:10.1109/tsp.2013.6614027Marasco, P. D., Hebert, J. S., Sensinger, J. W., Shell, C. E., Schofield, J. S., Thumser, Z. C., … Orzell, B. M. (2018). Illusory movement perception improves motor control for prosthetic hands. Science Translational Medicine, 10(432). doi:10.1126/scitranslmed.aao6990Mastinu, E., Doguet, P., Botquin, Y., Hakansson, B., & Ortiz-Catalan, M. (2017). Embedded System for Prosthetic Control Using Implanted Neuromuscular Interfaces Accessed Via an Osseointegrated Implant. IEEE Transactions on Biomedical Circuits and Systems, 11(4), 867-877. doi:10.1109/tbcas.2017.2694710Igual, C., Igual, J., Hahne, J. M., & Parra, L. C. (2019). Adaptive Auto-Regressive Proportional Myoelectric Control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(2), 314-322. doi:10.1109/tnsre.2019.2894464Huang, Y., Englehart, K. B., Hudgins, B., & Chan, A. D. C. (2005). A Gaussian Mixture Model Based Classification Scheme for Myoelectric Control of Powered Upper Limb Prostheses. IEEE Transactions on Biomedical Engineering, 52(11), 1801-1811. doi:10.1109/tbme.2005.856295Hahne, J. M., Dahne, S., Hwang, H.-J., Muller, K.-R., & Parra, L. C. (2015). Concurrent Adaptation of Human and Machine Improves Simultaneous and Proportional Myoelectric Control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(4), 618-627. doi:10.1109/tnsre.2015.240113

    Real-time agreement and fulfilment of SLAs in Cloud Computing environments

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    A Cloud Computing system must readjust its resources by taking into account the demand for its services. This raises the need for designing protocols that provide the individual components of the Cloud architecture with the ability to self-adapt and to reach agreements in order to deal with changes in the services demand. Furthermore, if the Cloud provider has signed a Service Level Agreement (SLA) with the clients of the services that it offers, the appropriate agreement mechanism has to ensure the provision of the service contracted within a specified time. This paper introduces real-time mechanisms for the agreement and fulfilment of SLAs in Cloud Computing environments. On the one hand, it presents a negotiation protocol inspired by the standard WSAgreement used in web services to manage the interactions between the client and the Cloud provider to agree the terms of the SLA of a service. On the other hand, it proposes the application of a real-time argumentation framework for redistributing resources and ensuring the fulfilment of these SLAs during peaks in the service demand.This work is supported by the Spanish government Grants CONSOLIDER-INGENIO 2010 CSD2007-00022, TIN2011-27652-C03-01, TIN2012-36586-C03-01 and TIN2012-36586-C03-03.De La Prieta, F.; Heras Barberá, SM.; Palanca Cámara, J.; Rodríguez, S.; Bajo, J.; Julian Inglada, VJ. (2014). Real-time agreement and fulfilment of SLAs in Cloud Computing environments. AI Communications. 1-24. doi:10.3233/AIC-140626S124[1]V. Aleven and K.D. Ashley, Teaching case-based argumentation through a model and examples, empirical evaluation of an intelligent learning environment, in: Artificial Intelligence in Education, AIED-97, Frontiers in Artificial Intelligence and Applications, Vol. 39, IOS Press, 1997, pp. 87–94.[2]M. Alhamad, W. Perth, T. Dillon and E. Chang, Conceptual SLA framework for cloud computing, in: 4th IEEE International Conference on Digital Ecosystems and Technologies (DEST), IEEE Press, 2010, pp. 606–610.Armbrust, M., Stoica, I., Zaharia, M., Fox, A., Griffith, R., Joseph, A. D., … Rabkin, A. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50. doi:10.1145/1721654.1721672Ashley, K. D. (1991). Reasoning with cases and hypotheticals in HYPO. International Journal of Man-Machine Studies, 34(6), 753-796. doi:10.1016/0020-7373(91)90011-u[6]P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt and A. Warfield, Xen and the art of virtualization, in: 9th ACM Symposium on Operating Systems Principles (SOSP-03), ACM Press, 2003, pp. 164–177.Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing. Future Generation Computer Systems, 28(5), 755-768. doi:10.1016/j.future.2011.04.017[8]A. Beloglazov and R. Buyya, Energy efficient allocation of virtual machines in cloud data centers, in: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, IEEE Computer Society, 2010, pp. 577–578.[9]A. Beloglazov and R. Buyya, Energy efficient resource management in virtualized cloud data centers, in: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, IEEE Computer Society, 2010, pp. 826–831.Bench-Capon, T., & Sartor, G. (2003). A model of legal reasoning with cases incorporating theories and values. Artificial Intelligence, 150(1-2), 97-143. doi:10.1016/s0004-3702(03)00108-5[11]T.J. Bench-Capon, Specification and implementation of Toulmin dialogue game, in: International Conferences on Legal Knowledge and Information Systems, JURIX-98, Frontiers of Artificial Intelligence and Applications, IOS Press, 1998, pp. 5–20.[12]R. Buyya, R. Ranjan and R.N. Calheiros, Intercloud: Utility-oriented federation of cloud computing environments for scaling of application services, in: 10th International Conference on Algorithms and Architectures for Parallel Processing – Volume Part I, ICA3PP’10, Springer-Verlag, 2010, pp. 13–31.[13]R. Buyya, C.S. Yeo and S. Venugopal, Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities, in: High Performance Computing and Communications, 2008. HPCC’08. 10th IEEE International Conference, September 2008, IEEE, 2008, pp. 5–13.Chen, C., Li, S. S., Chen, B., & Wen, D. (2011). Agent Recommendation for Agent-Based Urban-Transportation Systems. IEEE Intelligent Systems, 26(6), 77-81. doi:10.1109/mis.2011.94[15]Y.Y. Cheng, M. Low, S. Zhou, W. Cai and C.S. Choo, Evolving agent-based simulations in the clouds, in: 3rd International Workshop on Advanced Computational Intelligence (IWACI), 2010, pp. 244–249.[16]F. Dignum and H. Weigand, Communication and Deontic Logic, in: Information Systems – Correctness and Reusability. Selected Papers from the IS-CORE Workshop, R. Wieringa and R. Feenstra, eds, World Scientific Publishing Co., 1995, pp. 242–260.Erdogmus, H. (2009). Cloud Computing: Does Nirvana Hide behind the Nebula? IEEE Software, 26(2), 4-6. doi:10.1109/ms.2009.31[19]J.O. Fitó, I. Goiri and J. Guitart, SLA-driven elastic cloud hosting provider, in: 18th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), IEEE Computer Society, 2010, pp. 111–118.Fuentes-Fernández, R., Hassan, S., Pavón, J., Galán, J. M., & López-Paredes, A. (2012). Metamodels for role-driven agent-based modelling. Computational and Mathematical Organization Theory, 18(1), 91-112. doi:10.1007/s10588-012-9110-5Heras, S., Botti, V., & Julián, V. (2009). Challenges for a CBR framework for argumentation in open MAS. The Knowledge Engineering Review, 24(4), 327-352. doi:10.1017/s0269888909990178Heras, S., Jordán, J., Botti, V., & Julián, V. (2013). Argue to agree: A case-based argumentation approach. International Journal of Approximate Reasoning, 54(1), 82-108. doi:10.1016/j.ijar.2012.06.005[24]M. Jensen, J. Schwenk, N. Gruschka and L. Iacono, On technical security issues in cloud computing, in: IEEE International Conference on Cloud Computing, IEEE Press, 2009, pp. 109–116.Kakas, A., Maudet, N., & Moraitis, P. (2005). Modular Representation of Agent Interaction Rules through Argumentation. Autonomous Agents and Multi-Agent Systems, 11(2), 189-206. doi:10.1007/s10458-005-2176-4[26]M.J. Kim, H.G. Yoon and H.K. Lee, MAV: An intelligent Multi-agent model based on Cloud computing for resource virtualization, in: Computers, Networks, Systems, and Industrial Engineering, Studies in Computational Intelligence, Vol. 365, Springer, 2011, pp. 99–111.Kraus, S., Sycara, K., & Evenchik, A. (1998). Reaching agreements through argumentation: a logical model and implementation. Artificial Intelligence, 104(1-2), 1-69. doi:10.1016/s0004-3702(98)00078-2[28]W.-Y. Lin, G.-Y. Lin and H.-Y. Wei, Dynamic auction mechanism for cloud resource allocation, in: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, CCGRID’10, IEEE Computer Society, Washington, DC, USA, 2010, pp. 591–592.[29]S. Liu, G. Quan and S. Ren, On-line scheduling of real-time services for cloud computing, in: 6th World Congress on Services, SERVICES’10, IEEE Computer Society, 2010, pp. 459–464.Navarro, M., Heras, S., Botti, V., & Julián, V. (2013). Towards real-time agreements. Expert Systems with Applications, 40(10), 3906-3917. doi:10.1016/j.eswa.2012.12.087Ontañón, S., & Plaza, E. (2011). An argumentation framework for learning, information exchange, and joint-deliberation in multi-agent systems1. Multiagent and Grid Systems, 7(2-3), 95-108. doi:10.3233/mgs-2011-0169Palanca, J., Navarro, M., García-Fornes, A., & Julian, V. (2013). Deadline prediction scheduling based on benefits. Future Generation Computer Systems, 29(1), 61-73. doi:10.1016/j.future.2012.05.007[33]C. Pautasso, O. Zimmermann and F. Leymann, Restful web services vs. “big”’ web services: making the right architectural decision, in: Proceedings of the 17th International Conference on World Wide Web, WWW’08, ACM, New York, NY, USA, 2008, pp. 805–814.[34]J. Peng, X. Zhang, Z. Lei, B. Zhang, W. Zhang and Q. Li, Comparison of several cloud computing platforms, in: 2nd International Symposium on Information Science and Engineering, ISISE’09, IEEE Computer Society, 2009, pp. 23–27.Prakken, H., & Sartor, G. (1998). Artificial Intelligence and Law, 6(2/4), 231-287. doi:10.1023/a:1008278309945[36]I. Rahwan and G. Simari, eds, Argumentation in Artificial Intelligence, Springer, 2009.Ross, J. W., & Westerman, G. (2004). Preparing for utility computing: The role of IT architecture and relationship management. IBM Systems Journal, 43(1), 5-19. doi:10.1147/sj.431.0005Schaffer, H. E. (2009). X as a Service, Cloud Computing, and the Need for Good Judgment. IT Professional, 11(5), 4-5. doi:10.1109/mitp.2009.112[39]K.M. Sim, Agent-based cloud commerce, in: IEEE International Conference on Industrial Engineering and Engineering Management, IEEE Press, 2009, pp. 717–721.Soh, L.-K., & Tsatsoulis, C. (2005). A Real-Time Negotiation Model and A Multi-Agent Sensor Network Implementation. Autonomous Agents and Multi-Agent Systems, 11(3), 215-271. doi:10.1007/s10458-005-0539-5Talia, D. (2012). Clouds Meet Agents: Toward Intelligent Cloud Services. IEEE Internet Computing, 16(2), 78-81. doi:10.1109/mic.2012.28Tolchinsky, P., Modgil, S., Atkinson, K., McBurney, P., & Cortés, U. (2011). Deliberation dialogues for reasoning about safety critical actions. Autonomous Agents and Multi-Agent Systems, 25(2), 209-259. doi:10.1007/s10458-011-9174-5[44]A. Toniolo, T. Norman and K. Sycara, An empirical study of argumentation schemes in deliberative dialogue, in: 20th European Conference on Artificial Intelligence, ECAI-12, Frontiers in Artificial Intelligence and Applications, Vol. 242, IOS Press, 2012, pp. 756–761.[45]W.-T. Tsai, Q. Shao, X. Sun and J. Elston, Real-time service-oriented cloud computing, in: IEEE 6th World Congress on Services, SERVICES’10, IEEE Press, 2010, pp. 473–478.[46]D. Walton, C. Reed and F. Macagno, Argumentation Schemes, Cambridge University Press, 2008.[47]L. Wang, J. Tao, M. Kunze, A. Castellanos, D. Kramer and W. Karl, Scientific cloud computing: Early definition and experience, in: 10th IEEE International Conference on High Performance Computing and Communications (HPCC-08), IEEE Press, 2008, pp. 825–830.[48]Y.O. Yazir, C. Matthews, R. Farahbod, S. Neville, A. Guitouni, S. Ganti and Y. Coady, Dynamic resource allocation in computing clouds using distributed multiple criteria decision analysis, in: IEEE 3rd International Conference on Cloud Computing (CLOUD), IEEE Computer Society, 2010, pp. 91–98.[49]Y. Yu, S. Ren, N. Chen and X. Wang, Profit and penalty aware (pp-aware) scheduling for tasks with variable task execution time, in: ACM Symposium on Applied Computing, SAC’10, ACM, 2010, pp. 334–339

    Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: A survey

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    Copyright © 2013 Jun Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61329301, 61333012, 61374127 and 11301118, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant no. GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

    Fuzzy-logic-based control, filtering, and fault detection for networked systems: A Survey

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    This paper is concerned with the overview of the recent progress in fuzzy-logic-based filtering, control, and fault detection problems. First, the network technologies are introduced, the networked control systems are categorized from the aspects of fieldbuses and industrial Ethernets, the necessity of utilizing the fuzzy logic is justified, and the network-induced phenomena are discussed. Then, the fuzzy logic control strategies are reviewed in great detail. Special attention is given to the thorough examination on the latest results for fuzzy PID control, fuzzy adaptive control, and fuzzy tracking control problems. Furthermore, recent advances on the fuzzy-logic-based filtering and fault detection problems are reviewed. Finally, conclusions are given and some possible future research directions are pointed out, for example, topics on two-dimensional networked systems, wireless networked control systems, Quality-of-Service (QoS) of networked systems, and fuzzy access control in open networked systems.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374039, 61473163, and 61374127, the Hujiang Foundation of China under Grants C14002 andD15009, the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

    Summarization of Spanish Talk Shows with Siamese Hierarchical Attention Networks

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    [EN] In this paper, we present an approach to Spanish talk shows summarization. Our approach is based on the use of Siamese Neural Networks on the transcription of the show audios. Specifically, we propose to use Hierarchical Attention Networks to select the most relevant sentences for each speaker about a given topic in the show, in order to summarize his opinion about the topic. We train these networks in a siamese way to determine whether a summary is appropriate or not. Previous evaluation of this approach on summarization task of English newspapers achieved performances similar to other state-of-the-art systems. In the absence of enough transcribed or recognized speech data to train our system for talk show summarization in Spanish, we acquire a large corpus of document-summary pairs from Spanish newspapers and we use it to train our system. We choose this newspapers domain due to its high similarity with the topics addressed in talk shows. A preliminary evaluation of our summarization system on Spanish TV programs shows the adequacy of the proposal.This work has been partially supported by the Spanish MINECO and FEDER founds under project AMIC (TIN2017-85854-C4-2-R). Work of Jose-Angel Gonzalez is financed by Universitat Politecnica de Valencia under grant PAID-01-17.González-Barba, JÁ.; Hurtado Oliver, LF.; Segarra Soriano, E.; García-Granada, F.; Sanchís Arnal, E. (2019). Summarization of Spanish Talk Shows with Siamese Hierarchical Attention Networks. Applied Sciences. 9(18):1-13. https://doi.org/10.3390/app9183836S113918Carbonell, J., & Goldstein, J. (1998). The use of MMR, diversity-based reranking for reordering documents and producing summaries. Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR ’98. doi:10.1145/290941.291025Erkan, G., & Radev, D. R. (2004). LexRank: Graph-based Lexical Centrality as Salience in Text Summarization. Journal of Artificial Intelligence Research, 22, 457-479. doi:10.1613/jair.1523Lloret, E., & Palomar, M. (2011). Text summarisation in progress: a literature review. Artificial Intelligence Review, 37(1), 1-41. doi:10.1007/s10462-011-9216-zSee, A., Liu, P. J., & Manning, C. D. (2017). Get To The Point: Summarization with Pointer-Generator Networks. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). doi:10.18653/v1/p17-1099Narayan, S., Cohen, S. B., & Lapata, M. (2018). Ranking Sentences for Extractive Summarization with Reinforcement Learning. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). doi:10.18653/v1/n18-1158González, J.-Á., Segarra, E., García-Granada, F., Sanchis, E., & Hurtado, L.-F. (2019). Siamese hierarchical attention networks for extractive summarization. Journal of Intelligent & Fuzzy Systems, 36(5), 4599-4607. doi:10.3233/jifs-179011Furui, S., Kikuchi, T., Shinnaka, Y., & Hori, C. (2004). Speech-to-Text and Speech-to-Speech Summarization of Spontaneous Speech. IEEE Transactions on Speech and Audio Processing, 12(4), 401-408. doi:10.1109/tsa.2004.828699Shih-Hung Liu, Kuan-Yu Chen, Chen, B., Hsin-Min Wang, Hsu-Chun Yen, & Wen-Lian Hsu. (2015). Combining Relevance Language Modeling and Clarity Measure for Extractive Speech Summarization. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(6), 957-969. doi:10.1109/taslp.2015.2414820Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., & Hovy, E. (2016). Hierarchical Attention Networks for Document Classification. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. doi:10.18653/v1/n16-1174Conneau, A., Kiela, D., Schwenk, H., Barrault, L., & Bordes, A. (2017). Supervised Learning of Universal Sentence Representations from Natural Language Inference Data. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. doi:10.18653/v1/d17-1070Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391-407. doi:10.1002/(sici)1097-4571(199009)41:63.0.co;2-

    Variance-constrained multiobjective control and filtering for nonlinear stochastic systems: A survey

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    The multiobjective control and filtering problems for nonlinear stochastic systems with variance constraints are surveyed. First, the concepts of nonlinear stochastic systems are recalled along with the introduction of some recent advances. Then, the covariance control theory, which serves as a practical method for multi-objective control design as well as a foundation for linear system theory, is reviewed comprehensively. The multiple design requirements frequently applied in engineering practice for the use of evaluating system performances are introduced, including robustness, reliability, and dissipativity. Several design techniques suitable for the multi-objective variance-constrained control and filtering problems for nonlinear stochastic systems are discussed. In particular, as a special case for the multi-objective design problems, the mixed H 2 / H ∞ control and filtering problems are reviewed in great detail. Subsequently, some latest results on the variance-constrained multi-objective control and filtering problems for the nonlinear stochastic systems are summarized. Finally, conclusions are drawn, and several possible future research directions are pointed out
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