Hajid Alsubaie

Hajid Alsubaie

Associate Professor & Head of the Mechatronics Division, Department of Mechanical Engineering

Taif University, Taif, Saudi Arabia

Research Consultant, Applied Mathematics and Computational Sciences, KAUST

Visiting Researcher (2025), Electrical and Electronic Engineering Department, Imperial College London

Links: Email · Google Scholar · ORCID

Al-Hawiyah – Taif, Mecca, Saudi Arabia

Current Research

Theoretical work on model-free, decentralized multi-agent reinforcement learning in discounted Markov potential games, where agents learn from local bandit feedback without a common prior or explicit communication. Value-function learning and policy updates run on two timescales via asynchronous actor-critic methods. The aim is large-scale asymptotic and convergence analysis under weaker assumptions, with robustness and safety cast as mathematical constraints that yield provable guarantees.

Previous Research Interests

• Control Theory
• Nonlinear Vibrations

Employment

• 2026–Present Research Consultant, Applied Mathematics and Computational Sciences, KAUST.
• 10 Jan – 15 Dec 2025 Visiting Researcher, Imperial College London, Electrical and Electronic Engineering Department, London, UK.
• 2024–Present Head of the Mechatronics Division, Taif University, Mechanical Engineering Department, Taif.
• 2024–Present Associate Professor, Taif University, Mechanical Engineering Department, Taif.
• 2017–2023 Assistant Professor, Taif University, Mechanical Engineering Department, Taif.
• 2010–2016 Lecturer, Taif University, Mechanical Engineering Department, Taif.

Education

• 2010–2016 Ph.D., Mechanical Engineering, University of Maryland, College Park, MD, USA.

Selected Journal Publications

1. A novel numerical dynamics of fractional derivatives involving singular and nonsingular kernels: designing a stochastic cholera epidemic model, AIMS Mathematics.
2. A Model-Free Control Scheme for Rehabilitation Robots: Integrating Real-Time Observations with a Deep Neural Network for Enhanced Control and Reliability, Mathematics.
3. Recurrent Neural Network with Finite Time Sampling for Dynamics Identification in Rehabilitation Robots, Mathematics.
4. A neural state-space-based model predictive technique for effective vibration control in nano-beams, Frontiers in Physics.
5. Stabilization of Nonlinear Vibration of a Fractional-Order Arch MEMS Resonator Using a New Disturbance-Observer-Based Finite-Time Sliding Mode Control, Mathematics.
6. Fault-Tolerant Terminal Sliding Mode Control with Disturbance Observer for Vibration Suppression in Non-Local Strain Gradient Nano-Beams, Mathematics.
7. Stochastic Fixed-Time Tracking Control for the Chaotic Multi-Agent-Based Supply Chain Networks with Nonlinear Communication, Electronics.
8. Control of a Hydraulic Generator Regulating System Using Chebyshev-Neural-Network-Based Non-Singular Fast Terminal Sliding Mode Method, Mathematics.
9. Disturbance Attenuation Trajectory Tracking Control of Unmanned Surface Vessel Subject to Measurement Biases, Axioms.
10. Neural Learning Control Methodology for Predefined-Time Synchronization of Unknown Chaotic Systems, Fractals.
11. Hidden Homogeneous Extreme Multistability of a Fractional-Order Hyperchaotic Discrete-Time System, Symmetry.
12. A Numerical Confirmation of a Fractional-Order COVID-19 Model’s Efficiency, Symmetry.
13. Adaptive Discontinuous Control for Fixed-Time Consensus of Nonlinear Multi-Agent Systems, Electronics.
14. Kriging-based Model Predictive Control for Lower-limb Rehabilitation, Journal of Disability Research.

Teaching

• Nonlinear Control — Nonlinear Systems, Hassan Khalil.
• Control Systems — Feedback Systems, Åström & Murray.
• Theory of Systems — Linear Systems Theory, João Hespanha.
• Dynamics — Fundamentals of Applied Dynamics, Williams James.
• Vibrations — Fundamentals of Vibrations, Leonard Meirovitch.
• Real Analysis — Principles of Mathematical Analysis, Walter Rudin.

Senior Theses Supervised

• 2025–2026 Attitude Stabilization and Performance Tuning of a Quadrotor Drone.
• 2024–2025 Advancing Central Pattern Generator-Based Adaptive Network for Human-Intention Aligned Assistive Control.
• 2023–2024 A Grasshopper Model for Balance Landing.
• 2022–2023 Woodpecker’s Brain Impact Analysis.
• 2020–2021 A Leaf-Stem-Raindrop Dynamical System: A Bio-inspired Motion.

Favorite Quotes

"A degree in mathematics is a license to explore the universe."

In 1985, John Hubbard was asked to testify before the Committee on Science and Technology of the U.S. House of Representatives. He was preceded by a chemist from DuPont, who spoke of modeling molecules, and by an official from the geophysics institute of California, who spoke of exploring for oil and attempting to predict tsunamis.

When it was his turn, he explained that when chemists model molecules, they are solving Schrödinger's equation, that exploring for oil requires solving the Gelfand–Levitan equation, and that predicting tsunamis means solving the Navier–Stokes equation. Astounded, the chairman of the committee interrupted him and turned to the previous speakers. "Is that true, what Professor Hubbard says?" he demanded. "Is it true that what you do is solve equations?"