Haris Sheh Zad

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About

Haris Sheh Zad is a control systems researcher whose work focuses on the advanced stabilization and autonomous navigation of self-balancing two-wheeled robots. His primary research areas include model predictive control (MPC), adaptive control, and optimal controller design for nonlinear and unstable dynamical systems. Zad’s most notable contributions lie in developing robust and adaptive MPC frameworks that enable these inherently unstable platforms to maintain balance and track desired positions in real time. In his highly cited 2016 paper, he designed an optimal controller using a robust MPC scheme that computes future manipulated variable trajectories to optimize system behavior, garnering 11 citations. He further advanced this line of inquiry in 2017 by introducing an adaptive MPC that incorporates online model estimation, overcoming the limitations of traditional linear-time-invariant approaches and achieving 6 citations. By addressing the critical challenge of real-time adaptation to changing dynamics, Zad’s work has laid a foundation for more resilient and intelligent control in robotics. His research is particularly impactful for students and engineers working on autonomous vehicles, mobile robotics, and human-assistive technologies, where balancing and stability are paramount.