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About

Khaled Belarbi is a researcher whose work sits at the intersection of advanced control theory and robotics, with a primary focus on **nonlinear model predictive control (NMPC)** for fast-moving autonomous systems. His major contributions center on overcoming a fundamental challenge in the field: the computational bottleneck of solving complex optimization problems in real-time. Belarbi pioneered the use of **metaheuristic algorithms**—including particle swarm optimization—to accelerate NMPC, making it viable for applications previously considered too slow, such as quadcopter trajectory tracking under external perturbations and mobile robot navigation. His most influential work, "Nonlinear model predictive control of quadcopter" (2015, 32 citations), demonstrates a robust controller capable of handling disturbances, while his follow-up on mobile robots (30 citations) further validates the approach. Belarbi’s research is notable for pushing the boundaries of predictive control into multi-objective optimization, addressing both performance and computational efficiency. With a career spanning from foundational theory to practical implementation, his work has laid important groundwork for deploying sophisticated control strategies on resource-constrained robotic platforms, making him a key figure in the evolution of real-time autonomous systems.