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Ashish Mittal is a leading researcher in the fields of intelligent control systems, fractional-order control, and robotics. His work focuses on advancing the precision and adaptability of robotic manipulators, particularly under challenging conditions such as variable payloads. Mittal’s major contributions include the design of two-layered fractional-order fuzzy logic controllers and adaptive PID-like controllers enhanced by locally recurrent neural networks. These innovations significantly improve trajectory tracking and disturbance rejection in robotic systems. His most-cited paper, “Design of two-layered fractional order fuzzy logic controllers applied to robotic manipulator with variable payload” (2016), has garnered 59 citations, demonstrating its influence in the field. Another key work, “An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload” (2016), with 49 citations, further underscores his impact. Mittal’s research bridges theoretical advancements in fractional calculus and fuzzy logic with practical robotic applications, offering robust solutions for industrial automation. His notable achievement includes the optimum design of fractional-order hybrid fuzzy logic controllers, which has inspired further studies in adaptive control systems.