M. J. Mahmoodabadi
Papers
15
Total Citations
240
H-Index
8
About
M. J. Mahmoodabadi is a prominent researcher specializing in advanced control systems, robotics, and metaheuristic optimization. His work sits at the intersection of intelligent control theory and robotic locomotion, with a particular focus on biped robot tracking control and robust adaptive controllers for complex nonlinear systems. Mahmoodabadi has made significant contributions through the development of hybrid control strategies, most notably combining sliding mode control with PID, fuzzy logic, and feedback linearization approaches to address uncertainty and chaos in multi-input multi-output (MIMO) systems. A hallmark of his research is the application of multi-objective optimization algorithms — including Particle Swarm Optimization (PSO), Genetic Algorithms, and his own proposed Converged Teaching-Learning-Based Optimization (CTLBO) — to systematically tune controller parameters, eliminating the inefficiencies of traditional trial-and-error methods. His most cited work, an optimal robust sliding mode tracking controller for biped robots using multi-objective PSO (2013, 68 citations), exemplifies his approach of merging rigorous control theory with intelligent optimization. With a cumulative citation record spanning robotics, chaos control, and algorithm development, Mahmoodabadi's contributions provide researchers and engineers with powerful, practically implementable frameworks for controlling highly dynamic and uncertain robotic systems.
Research Focus
Key Achievements
Top Papers
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- 8CTLBO: Converged teaching–learning–based optimization9 citations · 2019
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