Welcome to Control Systems Laboratory Page!
We are an interdisciplinary research group advancing the frontiers of autonomous systems, advanced feedback theory, and intelligent automation. Our lab is based in the Department of Electrical and Computer Engineering.
The Control Systems Laboratory brings together researchers from control theory, robotics, computer science, and mechanical engineering to tackle complex engineering challenges that transcend traditional disciplinary boundaries. We focus on developing robust control frameworks, resilient estimation algorithms, and machine-learning-integrated feedback mechanisms to understand and optimize the behavior of complex dynamic systems.
Our research spans optimal control, cyber-physical system security, state estimation, and multi-agent coordination. We conduct high-fidelity simulations, develop theoretical architectures, and validate our methods on real-time hardware platforms to investigate core questions such as:
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How can autonomous systems maintain stability and performance under severe physical and cyber disturbances?
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How can dynamic state estimators be designed for complex, non-linear environments with uncertain noise characteristics?
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What algorithmic architectures enable resilient, distributed cooperation across fleets of unmanned vehicles and robotics platforms?
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How can physics-based control methodologies seamlessly integrate with modern data-driven learning techniques?
At the control systems lab, we foster an open-minded, hands-on, and collaborative research environment. We welcome inquiries from motivated students and researchers interested in joining our team or establishing new academic and industry partnerships.
Research Interest: Abdurrahman's research lies at the intersection of control theory, machine learning,
and cybersecurity for cyber-physical systems. His current work develops cyber-resilient
attack detection modeling architectures and robust state-estimation frameworks for
multi-UAV networks and autonomous ground vehicles. By combining physics-informed machine
learning with real-time anomaly detection, he aims to protect longitudinal tracking
and multi-agent coordination against false data injection (FDI) attacks, supporting
safe and reliable operation of autonomous systems that depend on networked sensing
and communication.
Research Interests:
My research focuses on the application of optimal control and intelligent resource management techniques to satellite communication (SATCOM) systems. In particular, I study how remote terminals can use control-based methods, such as Linear Quadratic Regulator (LQR), to dynamically generate bandwidth requests in response to changing traffic and queue conditions. My work also investigates resource allocation at the satellite system controller under communication delays, limited bandwidth, and finite buffer constraints using proportional, round-robin, and cooperative game-theoretic approaches such as the Shapley value. The overall goal of my research is to develop adaptive and efficient control strategies that reduce congestion and data loss while enhancing the performance of next-generation communication networks.
