His main research concerns Control engineering, Model predictive control, Control theory, Behavior change and Process control. His study looks at the relationship between Control engineering and fields such as Nonlinear system, as well as how they intersect with chemical problems. The Model predictive control study combines topics in areas such as Supply chain management and Inventory control.
He has included themes like Control and Estimator in his Control theory study. His study in Behavior change is interdisciplinary in nature, drawing from both Knowledge management, Digital health, Adaptation and Health promotion. Daniel E. Rivera performs multidisciplinary study in the fields of Process control and System identification via his papers.
Daniel E. Rivera mostly deals with System identification, Control theory, Control engineering, Model predictive control and Identification. His work deals with themes such as Transfer function, Estimation theory, Robust control, Machine learning and Process, which intersect with System identification. His Control theory study frequently links to other fields, such as Reduction.
In general Control engineering study, his work on PID controller often relates to the realm of Process control and Fractionating column, thereby connecting several areas of interest. His studies deal with areas such as Supply chain management and Operations research, Inventory control as well as Model predictive control. His Identification research includes elements of Control, Artificial intelligence, Algorithm and Estimator.
His primary areas of study are System identification, Identification, mHealth, Overweight and Model predictive control. His System identification research is multidisciplinary, incorporating perspectives in Control system, Dynamical systems theory, Control engineering, Artificial intelligence and Machine learning. His research in Control engineering intersects with topics in Setpoint and Autocorrelation.
His Identification study incorporates themes from Design of experiments, Transfer function, Control theory and Estimation. His work in the fields of Control theory, such as Multivariable calculus and Nonlinear system, intersects with other areas such as Basis function. His research investigates the connection with mHealth and areas like Social cognitive theory which intersect with concerns in Data modeling, Behavior change and Applied psychology.
Daniel E. Rivera mainly investigates Behavior change, Social cognitive theory, Data science, Health psychology and System identification. The Behavior change methods research Daniel E. Rivera does as part of his general Behavior change study is frequently linked to other disciplines of science, such as Personalization, therefore creating a link between diverse domains of science. His research integrates issues of Dynamical systems theory, Social psychology and Behavioral interventions in his study of Health psychology.
The various areas that Daniel E. Rivera examines in his System identification study include Machine learning, Model predictive control, Fibromyalgia and Artificial intelligence. His research investigates the link between Digital health and topics such as eHealth that cross with problems in Control engineering. His study brings together the fields of Control system and Control engineering.
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Internal model control: PID controller design
Daniel E. Rivera;Manfred Morari;Sigurd Skogestad.
Industrial & Engineering Chemistry Process Design and Development (1986)
Health behavior models in the age of mobile interventions: are our theories up to the task?
William T Riley;Daniel E Rivera;Audie A Atienza;Wendy Nilsen.
Translational behavioral medicine (2011)
Evaluating Digital Health Interventions: Key Questions and Approaches.
Elizabeth Murray;Eric B. Hekler;Gerhard Andersson;Gerhard Andersson;Linda M. Collins.
American Journal of Preventive Medicine (2016)
A MODEL PREDICTIVE CONTROL FRAMEWORK FOR ROBUST MANAGEMENT OF MULTI-PRODUCT, MULTI-ECHELON DEMAND NETWORKS
M. W. Braun;Daniel Rivera;W. M. Carlyle;K. G. Kempf.
Annual Reviews in Control (2002)
Simulation-based optimization of process control policies for inventory management in supply chains
Jay D. Schwartz;Wenlin Wang;Daniel E. Rivera.
Model predictive control strategies for supply chain management in semiconductor manufacturing
Wenlin Wang;Daniel E. Rivera;Karl G. Kempf.
International Journal of Production Economics (2007)
Using engineering control principles to inform the design of adaptive interventions: A conceptual introduction
Daniel E. Rivera;Michael D. Pew;Linda M. Collins.
Drug and Alcohol Dependence (2007)
Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research.
Donna Spruijt-Metz;Eric Hekler;Niilo Saranummi;Stephen Intille.
Translational behavioral medicine (2015)
Agile science: creating useful products for behavior change in the real world.
Eric B. Hekler;Predrag Klasnja;William T. Riley;Matthew P. Buman.
Translational behavioral medicine (2016)
"Plant-Friendly" system identification: a challenge for the process industries
Daniel E. Rivera;Hyunjin Lee;Martin W. Braun;Hans D. Mittelmann.
IFAC Proceedings Volumes (2003)
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