Amir Hajiloo
Papers
3
Total Citations
105
H-Index
2
About
Amir Hajiloo is a control systems researcher whose work sits at the intersection of robotics, optimization, and nonlinear dynamics. His primary research areas include model predictive control (MPC), visual servoing, and multi-objective control design for complex, constrained systems. Hajiloo’s most impactful contribution is his 2015 paper on robust online model predictive control for image-based visual servoing (IBVS), which has garnered 97 citations. In this work, he developed a controller that enables a 6-degree-of-freedom robotic system to handle input and output constraints in real time—a critical advance for autonomous manipulation in uncertain environments. His earlier work on passive bipedal walking robots (2005) demonstrates his long-standing interest in mechanical systems and motion analysis, using image processing to extract kinematic parameters from a robot moving on a declined surface. More recently, his 2016 paper on robust and multi-objective MPC for nonlinear systems addresses the challenge of balancing conflicting design specifications, a common hurdle in real-world engineering. Hajiloo’s research is notable for its practical focus on constraint handling and robustness, making his methods directly applicable to autonomous robotics and industrial control.
Research Focus
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Top Papers
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