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About

Pablo S. G. Cisneros is a control systems researcher whose work sits at the intersection of nonlinear control theory and practical engineering applications, with a particular focus on Nonlinear Model Predictive Control (NMPC) and Linear Parameter-Varying (LPV) representations. His research addresses one of the central challenges in modern control engineering: making advanced predictive control algorithms computationally tractable and experimentally viable for complex nonlinear systems. Cisneros has made notable contributions by bridging the gap between theoretical NMPC frameworks and real-world implementation. His landmark 2018 paper on constrained predictive control of a robotic manipulator — garnering 33 citations — demonstrated a quasi-LPV-based approach that achieved successful experimental deployment, a milestone that many NMPC researchers struggle to reach. His 2020 velocity-form NMPC scheme (30 citations) introduced an elegant solution for offset-free tracking and disturbance rejection, broadening the practical utility of predictive controllers. Further work on input-output quasi-LPV formulations and stability guarantees via control Lyapunov functions has strengthened the theoretical foundations of his methods. Collectively, his publications reflect a researcher deeply committed to developing control strategies that are both rigorously grounded and directly applicable to physical systems like robotic manipulators.