Rajendra Bhatt
Indian Institute of Technology Delhi, Drexel University, FMC (United States)
Papers
9
Total Citations
66
H-Index
4
About
Rajendra Bhatt is a robotics and intelligent control researcher whose work spans autonomous systems, adaptive control, and iterative learning control. His research has made meaningful contributions to the challenge of precise motion control in robotic systems, blending classical control theory with intelligent computational methods including fuzzy logic, wavelet series, Fourier series, and Hermite polynomial approximations. Bhatt's most influential work, "Adaptive stick–slip friction and backlash compensation using dynamic fuzzy logic system" (2005, 34 citations), addresses practical nonlinearities that plague industrial robotic systems, offering a dynamic fuzzy framework to achieve smoother, more reliable performance. His sustained interest in iterative learning control (ILC) led to a series of publications exploring how robots can refine trajectory tracking across repeated tasks — notably by incorporating prior experience into learning algorithms, a concept developed across multiple papers from 2006 to 2009. His earlier contributions in the late 1980s on multiresolutional knowledge representation for Intelligent Mobile Autonomous Systems (IMAS) reveal a long-standing commitment to autonomous robotics, anticipating many ideas central to modern robotic intelligence. Across his career, Bhatt's work reflects a thoughtful integration of mathematical rigor with practical engineering challenges in robotic control and autonomy.
Research Focus
Key Achievements
Top Papers
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- 7Simulation Of Dual Behavior Of An Autonomous System2 citations · 1990
- 8Learning in a multiresolutional conceptual framework2 citations · 2003
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