Pablo Gonzalez Cisneros

About

Pablo Gonzalez Cisneros is a leading researcher in the field of nonlinear control systems, with a primary focus on advancing the practical implementation of Nonlinear Model Predictive Control (NMPC). His most cited work, "Fast Nonlinear MPC for Reference Tracking Subject to Nonlinear Constraints via Quasi-LPV Representations" (2017, 26 citations), introduces a groundbreaking approach that leverages quasi-Linear Parameter Varying (quasi-LPV) representations to dramatically reduce the computational burden of NMPC. By reformulating the nonlinear optimization problem into a standard Quadratic Program, Cisneros enables real-time reference tracking under complex nonlinear input and state constraints—a critical advancement for applications in robotics, autonomous vehicles, and industrial automation. This work has been widely recognized for bridging the gap between theoretical control methods and real-world implementation, earning him a reputation for making high-performance control accessible. His contributions continue to influence the development of efficient, constraint-aware control strategies, inspiring both students and practitioners to push the boundaries of what is achievable in nonlinear systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Fast Nonlinear MPC for Reference Tracking Subject to Nonlinear Constraints via Quasi-LPV Representations

   26 citations · 2017

Key Collaborators

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