Meaghan Charest

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About

Meaghan Charest is a researcher specializing in nonlinear control systems and robotics, with a particular focus on model predictive control (MPC) strategies for complex mechanical systems. Her most cited work, "Non-linear model predictive control schemes with application on a 2 link vertical robot manipulator" (2016, 76 citations), introduces advanced MPC frameworks that address the challenges of real-time trajectory tracking and stability in underactuated robotic arms. This contribution has been instrumental in bridging theoretical control design with practical implementation, offering robust solutions for industrial automation and autonomous systems. Charest’s research demonstrates how nonlinear optimization techniques can enhance the precision and adaptability of robotic manipulators in dynamic environments. Her work has garnered attention from both control theory and robotics communities, with citations reflecting its impact on subsequent studies in predictive control and mechatronics. By integrating rigorous mathematical modeling with application-driven experiments, Charest has established herself as a key contributor to the development of intelligent, high-performance robotic systems. Her achievements underscore the growing importance of nonlinear MPC in advancing autonomous technologies.