Smriti Srivastava
Papers
6
Total Citations
208
H-Index
5
About
Smriti Srivastava is a researcher whose work sits at the vital intersection of intelligent control systems, neural networks, and nonlinear dynamics. Her primary contributions focus on developing advanced adaptive control strategies for complex, nonlinear systems—particularly robotic manipulators and benchmark problems like the inverted pendulum. Srivastava’s most impactful work, a 2017 paper on diagonal recurrent neural network-based adaptive control using Lyapunov stability criteria, has garnered 96 citations, establishing a rigorous framework for ensuring system stability. She has also made significant strides in comparative neural network modeling for dynamic systems (61 citations) and pioneered the application of bio-inspired optimization, such as the Whale Optimization Algorithm, to tune PID controllers for nonlinear plants. Her research directly addresses the challenge of controlling systems with unknown or partially known mathematical models, offering practical solutions for robotic arm control. While one of her papers was later retracted, her core body of work—spanning Gaussian radial basis function networks and neural network-based PID controllers—demonstrates a sustained focus on merging machine learning with classical control theory, providing a valuable toolkit for engineers tackling real-world nonlinear control problems.
Research Focus
Key Achievements
Top Papers
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- 5Whale Optimization Algorithm (WOA) Based Control Of Nonlinear Systems11 citations · 2019
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