Dr. Mohamed Tolba
Dr. Mohamed Tolba
I am a Lecturer in Mechanical and Aerospace Engineering with a background in aerospace engineering, specialising in nonlinear dynamics and control of underactuated mechanical systems. My research is method-centred and focuses on physics-based and safety-critical modelling and control, with representative applications in aerial robotics and autonomous mechanical platforms operating under uncertainty, constraints, and external disturbances. This work addresses challenges such as coupled dynamics, robustness, and adaptive autonomy, and includes the integration of physics-informed and learning-augmented methods to improve modelling fidelity and control performance while preserving formal guarantees.
In parallel, I pursue engineering education research focused on the evidence-based design, delivery, and evaluation of complex engineering learning experiences at scale. My education research emphasises quantitative evaluation methods and learning analytics to investigate how students develop deep conceptual and system-level understanding in mathematically intensive subjects such as dynamics, modelling, and control. Across both research and teaching, my work is guided by a common aim: bridging rigorous theory with practical implementation, whether in autonomous systems deployed in real-world settings or in preparing engineering graduates to reason effectively about complex systems.
Qualifications
- Doctor of Philosophy (Aerospace Engineering), Monash University, Australia, 2024
- Master of Science (Aerospace Engineering), Cairo University, Egypt, 2019
- Bachelor's Degree (Aerospace Engineering), Cairo University, Egypt, 2016
Expertise
- Nonlinear Dynamics and Underactuated Systems
Modelling and analysis of nonlinear mechanical systems with limited actuation, strong coupling, and complex dynamic behaviour, with a focus on developing control-oriented representations suitable for analysis and controller design.
- Physics-Based Modelling for Control
Development of first-principles, control-oriented models that balance modelling fidelity and tractability, enabling reliable stability analysis and controller synthesis for complex mechanical systems.
- Safety-Critical and Robust Control
Design of robust and adaptive control strategies for nonlinear systems operating under uncertainty, constraints, and external disturbances, with emphasis on stability, performance guarantees, and safe operation.
- Learning-Augmented and Physics-Informed Control
Integration of learning mechanisms with physics-based models to improve modelling accuracy and control performance, particularly through data-efficient adaptation and compensation of unmodelled dynamics while preserving control structure.
- Aerial Robotics and Aerial Manipulation
Application of advanced modelling and control methods to aerial robotic systems, including multirotor UAVs transporting and manipulating suspended payloads, as representative platforms for underactuated and safety-critical dynamics.
- Autonomous Systems under Uncertainty
Control and analysis of autonomous mechanical systems subject to parametric uncertainty, disturbances, and operational constraints, with applications across robotics and related autonomous platforms.
- Learning-Analytics-Informed Instructional Design
Use of data-driven analysis to study how students interact with instructional materials—such as educational videos—and to translate analytical insights into evidence-informed design decisions.