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About

Meysam Gheisarnejad is a control systems researcher whose work sits at the intersection of intelligent control theory, robotics, and advanced optimization techniques. His research focuses primarily on the motion control and trajectory tracking of wheeled mobile robots — complex nonholonomic systems notorious for their inherent nonlinearities, uncertainties, and sensitivity to external disturbances. Gheisarnejad's most influential contribution is his development of a deep reinforcement learning-based non-integer PID controller, published in 2020 and accumulating over 104 citations. This pioneering work applied the deep deterministic policy gradient algorithm to mobile robot tracking, demonstrating that fractional-order control combined with machine learning can substantially outperform conventional approaches in noisy, real-world conditions. Complementing this, his 2019 work on interval type-2 fuzzy logic supervised ANFIS controllers — cited 32 times — showcased his versatility in deploying soft computing strategies for velocity tracking tasks. More recently, Gheisarnejad has advanced robust sliding mode control frameworks that operate without kinematic equations, broadening applicability under dynamic uncertainty. Across his body of work, he consistently addresses practical implementation challenges, bridging theoretical control design with experimental validation — a quality that makes his research particularly valuable to engineers and roboticists working on real-world autonomous systems.