Glasgow Caledonian University
My research programme spans robust and optimal control theory, model predictive control, data-driven methods, and their applications in robotics, autonomous vehicles, renewable energy, and biomedical systems. I lead the Robotics for the Common Good research group and collaborate internationally across these domains.
Select a research theme below to explore related publications.
H-infinity control, sliding mode control, LMI-based design, backstepping control, and adaptive methods for uncertain systems with guaranteed performance bounds.
Tube-based robust MPC, stochastic MPC, event-triggered MPC, and computationally efficient algorithms for constrained systems under uncertainty.
Data-enabled predictive control (DeePC), data-driven H-infinity methods, learning-based observers, and neural-network-augmented controllers for systems with limited models.
UAV trajectory tracking, underwater manipulation, collaborative robots, mobile robot safety, and autonomous vehicle control with motion sickness mitigation.
AI-driven energy systems, Bayesian reliability assessment for wind turbines, solar energy optimisation with generative AI, and cyber-resilient control for power systems.
Active prosthetic control using fractional-order sliding modes, data-driven predictive control for targeted cancer therapy, and biomedical signal processing.