| 74. | Lopes dos Santos, P. M. Freigoun, C.A. Martin, D.E. Rivera, E.B. Hekler, R. Romano, and T.P. Perdicoúlis, “System identification of Just Walk: Using matchable-observable linear parametrizations,” IEEE Transactions on Control Systems Technology, Special Issue on System Identification and Control in Biomedical Applications, in press. | IEEE Xplore |
| 73. | Guo, P., D.E. Rivera, J.S. Savage, E.E. Hohman, A.M. Pauley, K.S. Leonard, and D. Symons Downs, “System identification approaches for energy intake estimation: enhancing interventions for managing gestational weight gain,” IEEE Transactions on Control Systems Technology, Special Issue on System Identification and Control in Biomedical Applications, early access, (2018), | IEEE Xplore |
| 72. | Freigoun, M.T., D.E. Rivera, P. Guo, E.E. Hohman, A.D. Gernand, D.S. Downs, and J.S. Savage, “A dynamical systems model of intrauterine fetal growth,” Mathematical and Computer Modelling of Dynamical Systems, 24:6, 661-687, | Taylor and Francis Online |
| 71. | Martin, C.A., D.E. Rivera, E.B. Hekler, W.T. Riley, M.P. Buman, M.A. Adams, and A.B. Magann, “Development of a control-oriented model of Social Cognitive Theory for optimized mHealth behavioral interventions,” IEEE Transactions on Control Systems Technology, early access, (2018), | IEEE Xplore |
| 70. | Pauley, A.M., E.E. Hohman, J.S. Savage, D.E. Rivera, P. Guo, K.S. Leonard, and D.S. Downs, “Gestational weight gain intervention impacts determinants of healthy eating and exercise in overweight/obese pregnant women,” Journal of Obesity, article ID 6469170, 2018, | Hindawi |
| 69. | Downs, D.S., J.S. Savage, D.E. Rivera, J. Smyth, B.J. Rolls, E.E. Hohman, K. McNiett, A.R. Kunselman, C. Stetter, A.M. Pauley, K.S. Leonard, and P. Guo, “Individually-tailored, adaptive intervention to manage gestational weight gain: protocol for a randomized controlled trial in women with overweight and obesity,” JMIR Research Protocols, Vol. 7, No. 6, (2018), | PubMed |
| 68. | Hekler, E.B., D.E. Rivera, C.A. Martin, S.S. Phatak, M.T. Freigoun, E. Korinek, P. Klasnja, M.A. Adams, and M.P. Buman, “Tutorial for using control systems engineering to optimize adaptive mobile health interventions,” Journal of Medical Internet Research, 20(6):e214, (2018) | PubMed |
| 67. | Phatak S.S., M.T. Freigoun, C.A. Martin, D.E. Rivera, E.V. Korinek, M.A. Adams, M.P. Buman, P. Klasnja, and E.B. Hekler, “Modeling individual differences: a case study for the application of system identification for personalizing a physical activity intervention,” Journal of Biomedical Informatics, Vol. 79, pgs. 82-97, 2018. | ScienceDirect |
| 66. | Korinek E.V., S.S. Phatak, C.A. Martin, M.T. Freigoun, D.E. Rivera, M.A. Adams, P. Klasnja, M.P. Buman, and E.B. Hekler, “Adaptive Step Goals and Rewards: A Longitudinal Growth Model of Daily Steps for a Smartphone-based Walking Intervention,” Journal of Behavioral Medicine. Vol. 41, No. 1, pgs. 74-86, 2018. | SpringerLink |
| 65. | Murray E., E.B. Hekler, G. Andersson, L.M. Collins, A. Doherty, C. Hollis, D.E. Rivera, R. West, J.C. Wyatt, “Evaluating digital health interventions: key questions and approaches,” American Journal of Preventive Medicine, 51 (5), 2016, pgs. 843-851. | AJPM |
| 64. | Riley, W.T., C.A. Martin, D.E. Rivera, E.B. Hekler, M.A. Adams, M.P. Buman, M. Pavel, A.C. King, “Development of a dynamic computational model of social cognitive theory,” Translational Behavioral Medicine, 6 (4), pp.483-495, 2016. | SpringerLink |
| 63. | Murray E., E.B. Hekler, G. Andersson, L.M. Collins, A. Doherty, C. Hollis, D.E. Rivera, R. West, J.C. Wyatt, “Evaluating digital health interventions: key questions and approaches,” American Journal of Preventive Medicine, 51 (5), 2016, pgs. 843-851. | ScienceDirect |
| 62. | Hekler, E.B., S. Michie, M. Pavel, D.E. Rivera, L.M. Collins, H.B. Jimison, C. Garnett, S. Parral, D. Spruijt-Metz, “Advancing models and theories for digital change interventions,” American Journal of Preventive Medicine, 51 (5), 2016, pgs. 825-832. | ScienceDirect |
| 61. | Hekler, E.B., P. Klasnja, W.T. Riley, M.P. Buman, J. Huberty, D.E. Rivera, and C.A. Martin, “Agile Science: creating useful products for behavior change in the real world.” Translational Behavioral Medicine , 6 (2), pp. 317-328, 2016. | SpringerLink |
| 60. | Diaz, J.M., S. Dormido, and D.E. Rivera, “ITTSAE: A set of interactive software tools for time series analysis education,” IEEE Control Systems, Vol. 36, No. 3, pgs. 112-120, 2016. | IEEE Xplore |
| 59. | Spruijt-Metz, D., E.Hekler, N. Saranummi, S. Intille, I. Korhonen, W. Nilsen, D.E. Rivera, B. Spring, S. Michie, D.A. Asch, A. Sanna, V. Traver Salcedo, R. Kukakfa, and M. Pavel, “Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research,” Translational Behavioral Medicine, Vol. 5, No. 3, pgs. 35-346, 2015. | SpringerLink |
| 58. | Deshpande, S. N.N. Nandola, D.E. Rivera, and J.W. Younger, “Optimized treatment of fibromyalgia using system identification and hybrid model predictive control, Control Engineering Practice, Vol. 33, pp. 161 – 173, 2014. | ScienceDirect |
| 57. | Deshpande, S. and D.E. Rivera, “Constrained optimal input signal design for data-centric estimation methods,” IEEE Transactions on Automatic Control, Vol. 59, No. 11, pp. 2990 – 2995, 2014. | IEEE Xplore |
| 56. | Deshpande, S., D.E. Rivera, J.W. Younger, and N. N. Nandola, “A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention,” Translational Behavioral Medicine, Vol 4, No. 3, pp. 275-289, 2014. Errratum in Vo. 4, No. 3, pp. 439, 2014. | SpringerLink |
| 55. | Schwartz, J.D. and D.E. Rivera, “A control-relevant approach to demand modeling for supply chain management,” Computers and Chemical Engineering, Vol. 70, pp 78 – 90, 2014. | ScienceDirect |
| 54. | Savage, J.S., D.S. Downs, Y. Dong, and D.E. Rivera, “Control systems engineering for optimizing a prenatal weight gain intervention to regulate infant birth weight,” American Journal of Public Health, Vol. 104, No. 7, pp. 1247-1254, July 2014. | APHA Publications |
| 53. | Pina, A.A., L.E. Holly, A.A. Zerr, and D.E. Rivera, “A personalized and control systems engineering conceptual approach to target childhood anxiety in the contexts of cultural diversity,” Journal of Clinical Child and Adolescent Psychology, Volume 43, Issue 3, pages 442-453, 2014. | Taylor and Francis Online |
| 52. | Timms, K.P., D.E. Rivera, L.M. Collins, and M.E. Piper, “Continuous-time system identification of a smoking cessation intervention,” International Journal of Control, Special Issue: Applications of Continuous-Time Model Identification and Estimation, Vol. 87, Issue 7, pgs. 1423-1437, 2014. | Taylor and Francis Online |
| 51. | Timms, K.P., D.E. Rivera, L.M. Collins, and M.E. Piper, “A dynamical systems approach to understand self-regulation in smoking cessation behavior change,” Nicotine and Tobacco Research, Special Issue on New Methods for Advancing Research on Tobacco Dependence Using Ecological Momentary Assessments, 16 (Suppl 2): S159 – S168, 2014. | Oxford Journals |
| 50. | Trail, J., L.M. Collins, D.E. Rivera, R. Li, and M. Piper “Functional data analysis for dynamical system identification of behavioral processes,” Psych. Methods, Vol 19(2), 175-187, 2014. | APA |
| 49. | Rivera, D.E. and H.B. Jimison, “Systems modeling of behavior change: two illustrations from optimized interventions for improved health outcomes,” IEEE Pulse, Vol. 4, No. 6, pp. 41 – 47, 2013. | IEEE Xplore |
| 48. | Hekler, E., M. Buman, N. Poothakandiyil, D.E. Rivera, J.M. Dzierzewski, A. Aiken-Morgan, C.S. McRae, B.L. Roberts, M. Marsiske, and P. Giacobbi, “Exploring Behavioral Markers of Long-term Physical Activity Maintenance: A Case Study of System Identification Modeling within a Behavioral Intervention,” Health Education and Behavior, Special Supplement on System Science Applications in Health Promotion and Public Health, 40(IS) 51S–62S, 2013. | Sage Journals |
| 47. | Alvarez, J.D., J.L. Guzmán, D.E. Rivera, M. Berenguel, and S. Dormido, “Perspectives on control-relevant identification through the use of interactive tools,” Control Engineering Practice, Volume 21, Issue 2, pages 171–183, February 2013. | ScienceDirect |
| 46. | Nandola, N.N. and D.E. Rivera, “An improved formulation of hybrid model predictive control with application to production-inventory systems,” IEEE Transactions on Control Systems Technology, Volume 21, No. 1, pages 121 – 135, January, 2013. | IEEE Xplore |
| 45. | Thomas, D.M., J.E. Navarro-Barrientos, D.E. Rivera, S.B. Heymsfield, C. Bredlau, L.M. Redman, C.K. Martin, S.A. Lederman, L.M. Collins, and N.F. Butte, “Dynamic energy-balance model predictiing gestational weight gain,” American Journal of Clinical Nutrition, Vol. 95, pgs. 115-122, January, 2012. | Amer. Soc. for Nutrition |
| 44. | Roche, K.T., D.E. Rivera, and J.K. Cochran, “A control engineering framework for managing whole hospital occupancy,” Mathematical and Computer Modelling, Vol. 55, Issues 3-4, pgs. 1401 – 1417, February, 2012. | ScienceDirect |
| 43. | Guzmán, J.L., D.E. Rivera, S. Dormido, and M. Berenguel, “An interactive software tool for system identification,” Advances in Engineering Software, Vol. 45, No. 1, pgs. 115-123, March 2012. | ScienceDirect |
| 42. | Riley, W.T., D.E. Rivera, A.A. Autienza, W. Nilsen, S. Allison, and R. Mermelstein,”Health behavior models in the age of mobile interventions: are our theories up to the task?” Translational Behavioral Medicine: Practice, Policy, Research, Vol. 1, No. 1, pgs. 53 – 71, March 2011. | SpringerLink |
| 41. | Steenis, R. and D.E. Rivera, “Plant-friendly signal generation for system identification using a modified simultaneous perturbation stochastic approximation (SPSA) methodology,” IEEE Transactions on Control Systems Technology, Vol. 19, No. 6, pgs. 1604-1612, November 2011. | IEEE Xplore |
| 40. | Navarro-Barrientos, J.E., D.E. Rivera, and L.M. Collins, “A dynamical model for describing behavioural interventions for weight loss and body composition change,” Mathematical and Computer Modelling of Dynamical Systems, Vol. 17, No. 2, pgs. 183-203, April 2011. | Taylor and Francis |
| 39. | Zafra-Cabeza, A., D.E. Rivera, L.M. Collins, M.A. Ridao, and E.F. Camacho, “A risk-based Model Predictive Control approach to adaptive interventions in behavioral health,” IEEE Transactions on Control Systems Technology, Vol. 19, No. 4, pgs. 891 – 901, July 2011. | IEEE Xplore |
| 38. | Schwartz, J.D. and D.E. Rivera, “A process control approach to tactical inventory management in production-inventory systems,” Int. J. Production Economics, Volume 125, Issue 1, Pages 111-124, May 2010. | ScienceDirect |
| 37. | Wang, W., D.E. Rivera, and H.D. Mittelmann, “Inner and outer loop optimization in semiconductor manufacturing supply chain management,” Computational Management Science, Vol. 6, No. 4, October 2009. | SpringerLink |
| 36. | Rivera, D.E., H. Lee, H.D. Mittelmann, and M.W. Braun, “Constrained multisine input signals for plant-friendly identification of chemical process systems,” Journal of Process Control, Vol. 19, No. 4, pgs. 623 – 635, April 2009. | Science Direct |
| 35. | Huang, D., H. Sarjoughian, W. Wang, G. Godding, D.E. Rivera, K.G. Kempf, and H.D. Mittelmann, “Simulation of semiconductor manufacturing supply-chain systems with DEVS, MPC, and KIB,” IEEE Transactions on Semiconductor Manufacturing, Vol. 22, No. 1, pgs. 164 – 174, February, 2009. | IEEE Xplore |
| 34. | Schwartz, J.D., M.R. Arahal, D.E. Rivera, and K.D. Smith, “Control-relevant demand forecasting for tactical decision-making in semiconductor manufacturing supply chain management,” IEEE Transactions on Semiconductor Manufacturing, Vol. 22, No. 1, pgs. 154 – 163, February, 2009. | IEEE Xplore |
| 33. | Wang, W. and D.E. Rivera, “A Model Predictive Control algorithm for tactical decision-making in semiconductor manufacturing supply chain management,” IEEE Transactions on Control Systems Technology, Vol. 16, No. 5, pgs. 841 – 855, September, 2008. | IEEE Xplore |
| 32. | Rivera-Flores, D.E., “Una metodología para la identificación integrada con el diseño de controladores IMC-PID,” (A methodology for integrated system identification with IMC-PID controller design), Revista Iberoamericana de Automática e Informática Industrial (Ibero-American Journal of Automatic Control and Industrial Computer Science), Vol. 4, No. 4, pgs. 129 – 134, October 2007. | ScienceDirect |
| 31. | Rivera, D.E., H. Lee, H.D. Mittelmann, and M.W. Braun, “High purity distillation: using plant-friendly multisine signals to identify a strongly interactive process,” Special Section on Applications of System Identification, IEEE Control Systems Magazine, Vol. 28, No. 5, pgs. 72-89, October 2007. | IEEE Xplore |
| 30. | Mittelmann, H.D., G. Pendse, D.E. Rivera, and H. Lee, “Optimization-based design of plant-friendly multisine signals using geometric discrepancy criteria,” Computational Optimization and Applications, Vol. 38, pgs. 173-190, 2007. | SpringerLink |
| 29. | Rivera, D.E., M.D. Pew, and L.M. Collins, “Using engineering control principles to inform the design of adaptive interventions: A conceptual introduction,” Drug and Alcohol Dependence, Special Issue on Adaptive Interventions, Vol. 88, Supplement 2, pgs. S31-S40, 2007. | Science Direct |
| 28. | Zafra-Cabeza, A., M.A. Ridao, E.F. Camacho, K.G. Kempf, and D.E. Rivera, “A stochastic predictive control approach applied to project risk management in semiconductor manufacturing,” Control Engineering Practice., Vol. 15, Issue 8, pgs. 969-984, 2007. | Science Direct |
| 27. | Wang, W., D.E. Rivera, and K.G. Kempf, “Model Predictive Control strategies for supply chain management in semiconductor manufacturing,” International Journal of Production Economics, Special Issue on Building Core Competence Through Operational Excellence, Vol. 107, Issue 1, pgs. 56-77, 2007. | Science Direct |
| 26. | Schwartz, J.D., W. Wang, and D.E. Rivera, “Optimal tuning of process control-based decision policies for inventory management in supply chains,” Automatica, Special Issue on Optimal Control Applications to Management Sciences, Vol. 42, pgs. 1311 – 1320, 2006. Note: No. 1 downloaded article in Automatica during July – Sept. 2006 | Science Direct |
| 25. | Rivera, D.E., “Teaching Semiphysical Modeling to ChE Students using a Brine-Water Mixing Tank Experiment,” Chemical Engineering Education, Vol. 39, No. 4, pgs. 308-315, 2005. | Reprint |
| 24. | Braun, M.W., D.E. Rivera, M.E. Flores, W.M.Carlyle, K.G. Kempf, “A Model Predictive Control Framework for Robust Management of Multi-Product, Multi-Echelon Demand Networks,” Annual Reviews in Control, Vol.27, Issue 2, pp. 229-245, 2003. | Science Direct |
| 23. | Vargas-Villamil, F.D., D.E. Rivera, and K.G. Kempf, “A Hierarchical Approach to Production Control of Reentrant Semiconductor Manufacturing Lines,” IEEE Transactions on Control Systems Technology, Vol. 11, No. 4, pp. 578-587, July 2003. | IEEE Xplore |
| 22. | Braun, M.W., D.E. Rivera, W.M. Carlyle, and K.G. Kempf, “Application of Model Predictive Control to Robust Management of Multi-Echelon Demand Networks in Semiconductor Manufacturing,” Simulation: Transactions of the Society for Modeling and Simulation International, Vol. 79, No. 3, pp.139-156, March 2003 | Simulation Preprint |
| 21. | Braun, M.W. R. Ortiz-Mojica, and D.E. Rivera, “Design of Minimum Crest Factor Multisinusoidal Signals for Plant-Friendly Identification of Nonlinear Process Systems,” Control Engineering Practice, Vol. 3, No. 3, pp. 301-313, March 2002. | Science Direct |
| 20. | Braun, M.W., D.E. Rivera, and A. Stenman. “A Model-on-Demand Identification Methodology for Nonlinear Process Systems,” International Journal of Control, Vol.74, Issue.18, pp.1708-1717, December 2001. | Taylor & Francis Online |
| 19. | Vargas-Villamil, F.D. and D.E. Rivera, “A Model Predictive Control Approach for Real-Time Optimization of Reentrant Manufacturing Lines,” Computers in Industry, 45, No. 1, pp. 45-57, June, 2001. | Science Direct |
| 18. | Ling, W.M. and D.E. Rivera, “Control-Relevant Nonlinear System Identification Using Restricted Complexity Models,” Journal of Process Control, 11, No. 2, pp. 209-222, 2001. | Science Direct |
| 17. | Stenman, A., F. Gustafsson, D.E. Rivera, L. Ljung, T. McKelvey, “On Adaptive Smoothing of Empirical Transfer Function Estimates,” Control Engineering Practice, 8, No. 2, pp. 1309-1315, 2000. | Science Direct |
| 16. | Vargas-Villamil, F.D. and D.E. Rivera, “Multilayer Optimization and Scheduling Using Model Predictive Control: Application to Reentrant Semiconductor Manufacturing Lines,” Computers and Chemical Engineering, 24, No. 2, pp. 2009-2021, 2000. | Science Direct |
| 15. | Rivera, D.E. and K.S. Jun, “An Integrated Identification and Control Design Methodology for Multivariable Process System Applications,” IEEE Control Systems Magazine, Special Issue on Process Control, 20, No. 3, pp. 25-37, June 2000. | IEEE Xplore |
| 14. | Ling, W.M. and D.E. Rivera, “Nonlinear Black-Box Identification of Distillation Column Models – Design Variable Selection for Model Performance Enhancement,” Applied Mathematics and Computer Science, Special Issue on Data Processing and Process Control, 8, No. 4, 1998. | Applied Mathematics and Computer Science |
| 13. | Ling, W.M. and D.E. Rivera, “Control-relevant Model Reduction of Volterra Series Models,” Journal of Process Control., 8, No. 2, pp 79-88, 1998. | Science Direct |
| 12. | Gaikwad, S.V. and D.E. Rivera, “Multivariable Frequency-Response Curvefitting with Application to Control-Relevant Parameter Estimation Problems,” Automatica, 33, No. 6, pp. 1169-1174, 1997. | Science Direct |
| 11. | Rivera, D.E., K.S. Jun, V.E. Sater, and M.K. Shetty, “Teaching Process Dynamics and Control Using an Industrial-Scale Real-Time Computing Environment,” Computer Applications in Engineering Education, Computer-Aided Chemical Engineering Education Special Issue, Vol. 4 No. 3, pp. 191-205, 1996. | Wiley Interscience |
| 10. | Rivera, D.E. and S. Gaikwad, “Digital PID Controller Design Using ARX Estimation,” Computers and Chemical Engineering, 20, No. 11, pp. 1317-1334, 1996. | Science Direct |
| 9. | Jun, Kyoung-Shik, D.E. Rivera, E. Elisante, and V.E. Sater. “A Computer-Aided Design Tool for Robustness Analysis and Control-Relevant Identification of Horizon Predictive Control with Application to a Binary Distillation Column,” Journal of Process Control, 6, No. 2-3, pp. 177-186, 1996. | Science Direct |
| 8. | Rivera, D.E. and S.V. Gaikwad. “Systematic Techniques for Determining Modeling Requirements for SISO and MIMO Feedback Control Problems,” Journal of Process Control 5, No. 4, pp.213-224, 1995. | Science Direct |
| 7. | Rivera, D.E., J.F. Pollard, and C.E. García, “Control-relevant prefiltering: a systematic design approach and case study, ” IEEE Trans. Autom. Cntrl., Special Issue on System Identification for Control Design, 37, 964, 1992. | IEEE Xplore |
| 6. | Rivera, D.E. and M. Morari, “Plant and controller reduction problems for closed-loop performance,” IEEE Trans. Autom. Cntrl., 37, 398, 1992. | IEEE Xplore |
| 5. | Rivera, D.E. and M. Morari, “Low-order SISO controller tuning methods for the H2, H-infinity and mu objective functions, ” Automatica, 26, 361, 1990. | Science Direct |
| 4. | Laughlin, D.L., D.E. Rivera, and M. Morari, “Smith Predictor design for robust performance,” Int. J. of Control, 46, 477, 1987. | Taylor & Francis Online |
| 3. | Holt, B.R., N.F. Jerome, D.E. Rivera, M. Morari, W.H. Ray et al., “CONSYD – integrated software for computer-aided control system design and analysis,” Computers and Chemical Engineering, 11, 187, 1987. | Science Direct |
| 2. | Rivera, D.E. and M. Morari, “Control-relevant model reduction problems for SISO H2, H-infinity and mu controller synthesis,” Int. J. Control, 46, 505, 1987. | Taylor & Francis Online |
| 1. | Rivera, D.E., M. Morari, and S. Skogestad, “Internal Model Control 4. PID controller design,” Ind. Eng. Chem. Proc. Des. and Dev., 25, 252, 1986. 21st most cited paper in I&EC Research since 1975. (http://pubs.acs.org/page/iecred/anniversary/100/100mostcited.html) | American Chemical Society |