Papers
10
Total Citations
166
H-Index
7
About
Anuj Nandanwar is a robotics and control systems researcher whose work sits at the intersection of sliding-mode control, multi-robot systems, and cyber-physical frameworks. His research primarily advances robust control strategies for nonholonomic mobile robots and multi-agent systems, with a particular focus on event-triggered mechanisms that reduce computational and communication overhead without sacrificing performance. Nandanwar's most influential contributions center on super-twisting sliding-mode algorithms. His 2020 work on multivariable event-triggered generalized super-twisting control for safe robot navigation (38 citations) and his 2021 exponential super-twisting algorithm incorporating fault estimation and uncertainty compensation (35 citations) have established him as a notable voice in robust robot path-tracking and safe navigation research. His work on finite-time consensus control for multi-robot systems (29 citations) further demonstrates his expertise in leader-follower multi-agent frameworks under disturbances. Beyond classical control theory, Nandanwar has explored stochastic event-based formation control, fuzzy-inference-based path planning, and fault-tolerant control within cyber-physical systems. More recently, he has ventured into human-robot interaction, investigating the use of conversational AI-enabled social robots to assess mental health indicators such as stress and anxiety. With over 160 cumulative citations across a focused research portfolio, his work reflects a consistent commitment to bridging advanced control theory with real-world autonomous robotic applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10