I am an undergraduate Mechanical Engineering student focused on control systems, nonlinear dynamics, and robotics, with particular interest in physically meaningful trajectory generation for nonlinear systems. My work sits at the intersection of optimal control, differential flatness, and rigid-body dynamics, emphasizing smooth, actuator-feasible force and torque profiles rather than reliance on feedback alone.

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My recent research applies Pontryagin’s Minimum Principle and physics-informed optimization to robotic manipulators. I developed a control pipeline that couples closed-form optimal trajectories with analytically derived Euler–Lagrange dynamics, enabling dynamically consistent motion planning without numerically intensive shooting or collocation methods. This work is available as an arXiv preprint and indexed across multiple academic platforms.

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I am especially interested in control problems constrained by real physical structure—actuator limits, inertia coupling, structural loading, and energy consistency. Rather than treating dynamics as a post-processing step, I embed them directly into trajectory synthesis so the resulting motion is both theoretically optimal and hardware-implementable.

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While fluent in classical feedback control, my primary interest is feedforward motion planning for mechanical and robotic systems, where trajectory structure is fundamental. I view feedback as a stabilizing layer around well-designed dynamics, not a substitute for them.

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I am seeking internships and research roles in control systems, robotics, and applied dynamics, involving linear and nonlinear state-space modeling and physically grounded control design.