Papers
11
Total Citations
233
H-Index
6
About
Srinivasan Alavandar is a robotics and control systems researcher whose work has made meaningful contributions to the intelligent control of robot manipulators. His research sits at the intersection of computational intelligence and robotic systems, with particular expertise in neuro-fuzzy methods, genetic algorithms, and bio-inspired optimization techniques applied to complex robotic control problems. His most influential contribution, "Neuro-Fuzzy based Approach for Inverse Kinematics Solution of Industrial Robot Manipulators" (2008, 98 citations), addressed one of robotics' most persistent challenges — solving inverse kinematics for nonlinear, high-complexity systems — by leveraging adaptive neuro-fuzzy inference systems to deliver practical, data-driven solutions. This work, alongside his fuzzy PD+I and hybrid adaptive neuro-fuzzy control studies published the same year, established a coherent body of research demonstrating the power of soft computing frameworks for managing the nonlinearities and uncertainties inherent in multi-degree-of-freedom manipulators. Alavandar further extended this work into flexible manipulator control and trajectory tracking, employing bacterial foraging optimization and genetic algorithm-tuned controllers to push performance boundaries. His research spans both industrial manipulators and emerging platforms such as hexapod robots, reflecting a sustained commitment to advancing intelligent, adaptive robotic systems across diverse applications.
Research Focus
Key Achievements
Top Papers
- 1
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- 3Fuzzy PD+I control of a six DOF robot manipulator30 citations · 2008
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- 6Genetic Fuzzy Based Tracking Control of 3 DOF Robot Arm10 citations · 2008
- 7
- 8GENETIC ALGORITHM BASED ROBOT MASSAGE5 citations · 2007
- 9Efficient PID Controller based Hexapod Wall Following Robot4 citations · 2019
- 10Control of Robot Manipulator Error Using FPDI–IQGA in Neural Network3 citations · 2016