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About

Rajpal Singh is a rising researcher at the forefront of data-driven control and nonlinear robotics. His work centers on three pivotal areas: Koopman operator theory for system identification, safe control via barrier functions, and adaptive control for redundant manipulators. Singh’s most influential contribution is his 2025 paper on “Adaptive Koopman embedding for robust control of nonlinear dynamical systems,” which has already garnered 8 citations. This work addresses a critical bottleneck in robotics—the synthesis of linear control techniques for inherently nonlinear systems—by proposing a data-driven method that overcomes the limitations of traditional Koopman algorithms. In parallel, his 2024 paper on “Approximation-Free Robust Tracking Control of Unknown Redundant Manipulators” (5 citations) introduces a novel neural control architecture that achieves prescribed tracking performance under joint constraints without requiring a robot model. His “Collision Cone Approach for Control Barrier Functions” (4 citations) unifies collision avoidance for ground and aerial vehicles, offering a practical safety framework. Singh’s overview paper on data-driven paradigms for robotic systems further underscores his role as a synthesizer of emerging methodologies. With a growing citation footprint and a focus on bridging theory and real-world constraints, Singh is a promising voice in modern robotics.