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- Teodoro Alamo

Discipline name
H-index
Citations
Publications
World Ranking
National Ranking

Electronics and Electrical Engineering
D-index
33
Citations
5,530
140
World Ranking
2724
National Ranking
44

- Control theory
- Algorithm
- Artificial intelligence

The scientist’s investigation covers issues in Control theory, Mathematical optimization, Model predictive control, Bounded function and Nonlinear system. His Control theory research incorporates themes from Computation and Invariant. The various areas that Teodoro Alamo examines in his Mathematical optimization study include Nonlinear control, Probabilistic logic and Randomized algorithm.

His work deals with themes such as Stability, Optimization problem, Control theory and Function, which intersect with Model predictive control. His Bounded function research is multidisciplinary, incorporating perspectives in Algorithm and State. Teodoro Alamo works mostly in the field of Linear system, limiting it down to topics relating to Offset and, in certain cases, Operating point.

- Brief Guaranteed state estimation by zonotopes (321 citations)
- Brief paper: MPC for tracking piecewise constant references for constrained linear systems (307 citations)
- Input-to-state stable MPC for constrained discrete-time nonlinear systems with bounded additive uncertainties (244 citations)

Teodoro Alamo mainly investigates Control theory, Model predictive control, Mathematical optimization, Linear system and Control theory. Much of his study explores Control theory relationship to Bounded function. As part of one scientific family, Teodoro Alamo deals mainly with the area of Bounded function, narrowing it down to issues related to the Algorithm, and often System identification.

His Model predictive control research focuses on Offset and how it connects with Operating point. In general Mathematical optimization study, his work on Optimization problem often relates to the realm of Convex optimization, thereby connecting several areas of interest. His work carried out in the field of Linear system brings together such families of science as Linear matrix inequality, Quadratic equation, Computation and Piecewise.

- Control theory (52.57%)
- Model predictive control (45.85%)
- Mathematical optimization (44.27%)

- Model predictive control (45.85%)
- Control theory (52.57%)
- Mathematical optimization (44.27%)

Model predictive control, Control theory, Mathematical optimization, Optimization problem and Control theory are his primary areas of study. His Model predictive control research includes themes of Stability, Process, Optimal control and Nonlinear system. His study connects Bounded function and Control theory.

His research integrates issues of Probabilistic logic and Scale in his study of Mathematical optimization. His Optimization problem study integrates concerns from other disciplines, such as Uncertainty analysis, Solver and Multivariable calculus. The study incorporates disciplines such as Penalty method, Exponential stability and Trajectory in addition to Control theory.

- Nonlinear MPC for Tracking Piece-Wise Constant Reference Signals (39 citations)
- MPC for Tracking Periodic References (34 citations)
- COVID-19: Open-data resources for monitoring, modeling, and forecasting the epidemic (24 citations)

- Control theory
- Artificial intelligence
- Algorithm

His primary scientific interests are in Control theory, Control theory, Model predictive control, Linear system and Trajectory. In Control theory, Teodoro Alamo works on issues like Optimization problem, which are connected to Robustness, Memory footprint and Multivariable calculus. The concepts of his Control theory study are interwoven with issues in Complex system, Bounded function and Differential equation.

As part of his studies on Model predictive control, he often connects relevant subjects like Nonlinear system. Teodoro Alamo interconnects Discrete time and continuous time and Exponential stability in the investigation of issues within Trajectory. His Probabilistic logic course of study focuses on Robust control and Mathematical optimization.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Brief Guaranteed state estimation by zonotopes

T. Alamo;J. M. Bravo;E. F. Camacho.

Automatica **(2005)**

484 Citations

Brief paper: MPC for tracking piecewise constant references for constrained linear systems

D. Limon;I. Alvarado;T. Alamo;E. F. Camacho.

Automatica **(2008)**

461 Citations

Input-to-state stable MPC for constrained discrete-time nonlinear systems with bounded additive uncertainties

D.L. Marruedo;T. Alamo;E.F. Camacho.

conference on decision and control **(2002)**

347 Citations

Input-to-State Stability: A Unifying Framework for Robust Model Predictive Control

D. Limon;T. Alamo;D. M. Raimondo;D. Muñoz de la Peña.

Lecture Notes in Control and Information Sciences **(2009)**

221 Citations

Robust tube-based MPC for tracking of constrained linear systems with additive disturbances

D. Limon;I. Alvarado;T. Alamo;E.F. Camacho.

Journal of Process Control **(2010)**

212 Citations

Input to state stability of min-max MPC controllers for nonlinear systems with bounded uncertainties

D. Limon;T. Alamo;F. Salas;E. F. Camacho.

Automatica **(2006)**

209 Citations

Model predictive control techniques for hybrid systems

E.F. Camacho;D.R. Ramirez;D. Limon;D. Muñoz de la Peña.

Annual Reviews in Control **(2009)**

184 Citations

On input-to-state stability of min-max nonlinear model predictive control

M Mircea Lazar;D Munoz de la Pena;Wpmh Maurice Heemels;T Alamo.

Systems & Control Letters **(2008)**

169 Citations

Randomized Strategies for Probabilistic Solutions of Uncertain Feasibility and Optimization Problems

T. Alamo;R. Tempo;E.F. Camacho.

IEEE Transactions on Automatic Control **(2009)**

164 Citations

On the stability of constrained MPC without terminal constraint

D. Limon;T. Alamo;F. Salas;E.F. Camacho.

IEEE Transactions on Automatic Control **(2006)**

156 Citations

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