Adil Zohaib

Papers

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About

Adil Zohaib’s research centers on control systems and robotics, with a particular focus on optimal and predictive control strategies for autonomous and self-balancing platforms. His most cited work, “Optimal Controller Design for Self-Balancing Two-Wheeled Robot System” (2016, 11 citations), introduces a robust Model Predictive Control (MPC) scheme that optimizes system behavior by computing future trajectories of manipulated variables. This contribution addresses critical challenges in stabilizing inherently unstable two-wheeled robots, offering a computationally efficient solution that balances performance and robustness. Zohaib’s approach demonstrates how MPC can be tailored for real-time applications in mobile robotics, bridging theoretical control design with practical implementation. His work has been cited by researchers exploring advanced control in mechatronics and autonomous vehicles, underscoring its relevance to the broader field of dynamic system stabilization. Beyond this paper, Zohaib’s research portfolio reflects a sustained interest in intelligent control methods, including adaptive and nonlinear techniques, aimed at enhancing the autonomy and reliability of robotic systems. His contributions provide a foundation for students and engineers seeking to apply predictive control in resource-constrained, real-world environments.