Bhathiya Rathnayake

Research Interests

• The ongoing transition to renewable energy has significantly increased the penetration of inverter-based resources (IBRs) in modern power systems. While IBRs provide benefits such as modularity, flexibility, and efficiency, they also introduce fundamental challenges, including reduced system inertia, vulnerability to disturbances, and complex nonlinear and hybrid dynamics. My current research focuses on developing safe and resilient distributed control frameworks for future inverter-dominated power systems. My goal is to establish mathematically rigorous frameworks that ensure provable safety and performance under uncertainty while reducing reliance on centralized coordination.
  
  In a broader sense, my research interests lie at the intersection of system modeling, control theory, and data-driven approaches. I am particularly excited about challenges involving complex, distributed, and safety-critical systems. I am always open to collaborations and opportunities domains such as
  • Power System Networks – Addressing decentralization, robustness, and safety in modern grids where hybrid and nonlinear phenomena are ubiquitous.
  • Automated Vehicles – Developing methods for safe, collision-free navigation and adaptability to diverse road geometries under uncertainty.
  • Continuum Robotics – Tackling challenges of underactuation, task-space control, and path planning for highly flexible robotic structures.
  If you share similar research interests or have ideas for joint projects, feel free to send me an email at brathna1@jh.edu for collaborations!

Past Research

• During my PhD research, I developed sampled-data and event-triggered control approaches for PDE systems using PDE backstepping. These approaches have applications in various domains, including battery thermal management, additive manufacturing, traffic flow control, and water flow control. Some key highlights of my research include:
• The first periodic event-triggered control approach with PDE backstepping
• The first self-triggered control approach with PDE backstepping
• Sparsification of event generation using the concept of a performance barrier
• The first global exponential stability result under dynamic event-triggered control with PDE backstepping
• The first event-triggered control design (full-state feedback) with PDE backstepping that requires only event-triggered measurements for the triggering mechanism