Carlos Sotelo
Papers
1
Total Citations
21
H-Index
1
About
Carlos Sotelo is a control systems engineer whose work centers on advancing nonlinear model predictive control (NMPC) for discrete-time systems. His most cited paper, "A Novel Discrete-time Nonlinear Model Predictive Control Based on State Space Model" (2018), has garnered 21 citations and introduces a streamlined framework that bridges theoretical rigor with practical implementation. Sotelo’s key contribution lies in developing a state-space-based NMPC approach that reduces computational complexity while maintaining stability and performance—a critical step for real-time applications in robotics, process control, and autonomous systems. By reformulating the optimization problem, his method enables more efficient handling of nonlinear dynamics and constraints, making advanced control accessible to industries where computational resources are limited. Though his citation count is modest, the work’s focus on bridging theory and application has resonated with peers seeking deployable solutions. Sotelo’s research continues to influence the design of robust, real-time controllers, positioning him as a thoughtful contributor to the evolution of predictive control methodologies.
Research Focus
Key Achievements
Top Papers
- 1