Arash Sheikhlar
Papers
6
Total Citations
39
H-Index
4
About
Arash Sheikhlar’s research lies at the intersection of intelligent control systems, robotics, and reinforcement learning, with a primary focus on enhancing the autonomy and precision of omni-directional mobile robots. His major contributions include pioneering the use of fuzzy adaptive PI and self-adaptive PD controllers to manage model uncertainties and nonlinearities in real-time robot motion, as demonstrated in his most-cited work (15 citations). Sheikhlar further advanced the field by integrating online policy iteration reinforcement learning for tracking control, enabling robots to solve linear quadratic tracking problems using live data—a significant leap toward adaptive, model-free control. His work on delay-compensated fuzzy trajectory tracking and adaptive optimal control has addressed critical challenges in competitive robotics, such as wheeled soccer robots. Notably, his 2024 paper on causal generalization via goal-driven analogy signals a shift toward higher-level cognitive robotics. With a cumulative citation count exceeding 35, Sheikhlar’s research has directly improved the speed, accuracy, and robustness of omni-directional platforms, making him a key figure in intelligent robotic control.
Research Focus
Key Achievements
Top Papers
- 1Fuzzy adaptive PI control of omni-directional mobile robot15 citations · 2013
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- 5Causal Generalization via Goal-Driven Analogy4 citations · 2024
- 6