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About

M. Vijay is a prominent researcher specializing in advanced control systems for robotic manipulators, with a particular focus on intelligent and adaptive control strategies. His work sits at the intersection of sliding mode control, neural networks, fuzzy logic, and evolutionary optimization techniques, making significant contributions to the field of nonlinear control engineering. Vijay's most impactful contribution, "Backstepping Terminal Sliding Mode Control of Robot Manipulator Using Radial Basis Functional Neural Networks" (2017), has garnered 53 citations, reflecting the research community's strong interest in his hybrid control architectures. His closely related work coupling Artificial Neuro Fuzzy Inference Systems (ANFIS) with sliding mode control for two-degree-of-freedom robot manipulators has accumulated 49 citations, establishing him as a leading voice in intelligent sliding mode methodologies. Across his body of work, Vijay consistently integrates bio-inspired optimization algorithms — including Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) — with classical and modern control frameworks to enhance robustness and adaptability. His research on overhead transmission line de-icing robot control further demonstrates his commitment to applying these methods to real-world engineering challenges. For students and researchers working on intelligent robotics and nonlinear control, Vijay's publications offer a rich foundation in combining computational intelligence with rigorous control theory.