Mongi Besbes

About

Mongi Besbes is a robotics researcher specializing in bipedal locomotion and model predictive control (MPC) for humanoid and anthropomorphic systems. His work focuses on generating stable walking trajectories and designing robust controllers that can handle real-world uncertainties and constraints. Besbes has made significant contributions to the synthesis of MPC controllers using linear matrix inequalities (LMI), ensuring closed-loop stability for systems subject to input and output constraints—a critical challenge in legged robotics. His most cited paper, "Trajectory Generation using Predictive PID Control for Stable Walking Humanoid Robot" (2015, 20 citations), introduces a predictive control strategy to compute center-of-mass trajectories, improving walking stability. Another key work applies LMI-based robust dynamic control to biped robots, optimizing behavior through the Zero Moment Point criterion. With total citations exceeding 35 across his top papers, Besbes’ research bridges theoretical control synthesis and practical robot walking, offering valuable insights for students and researchers in nonlinear control, constraint satisfaction, and humanoid locomotion.

Research Focus

Key Achievements

Top Papers

1. 1
   Trajectory Generation using Predictive PID Control for Stable Walking Humanoid Robot

   20 citations · 2015
2. 2
   Synthesise of MPC controller for uncertain systems subject to input and output constraints: application to anthropomorphic robot arm

   10 citations · 2019
3. 3
   Switched Control for the Walking of a Compass Gait Biped Robot

   3 citations · 2014
4. 4
   Synthesise of MPC controller for uncertain systems subject to input and output constraints: application to anthropomorphic robot arm

   2 citations · 2019
5. 5
   An LMI-based robust dynamic controller design for the improvement of robot behavior walk, ZMP based

   2 citations · 2015

Key Collaborators

Contact & Links