Papers
3
Total Citations
36
H-Index
3
About
Ruchika is a researcher in robotics and control systems, specializing in advanced trajectory tracking and motion control for robot manipulators. Her work focuses on integrating robust sliding mode control techniques with neural network architectures, particularly radial basis function neural networks (RBFNN), to address challenges like finite-time convergence, singularity avoidance, and disturbance rejection. Her most cited paper, "Finite time control scheme for robot manipulators using fast terminal sliding mode control and RBFNN" (2018, 23 citations), introduces a novel hybrid approach that achieves rapid, precise tracking while mitigating chattering and model uncertainties. This contribution is complemented by her 2019 study on non-singular terminal sliding mode control with H∞ performance (8 citations) and her 2021 extension integrating RBFNN for adaptive compensation (5 citations). Collectively, her work has garnered over 36 citations, demonstrating its relevance to both theoretical advancements and practical implementations in robotics. Ruchika’s research is particularly notable for bridging classical robust control with modern learning-based methods, offering scalable solutions for high-precision industrial and service robots. Her contributions are valuable for students and engineers seeking to understand how neural networks can enhance the reliability and speed of nonlinear control systems.
Research Focus
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Top Papers
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