Saleem Riaz
Papers
7
Total Citations
85
H-Index
6
About
Dr. Saleem Riaz is a leading researcher in advanced control systems, specializing in precision motion control for robotics and industrial automation. His work centers on developing novel adaptive and predefined-time convergent control strategies to address critical challenges in servo systems, particularly those involving Permanent Magnet Synchronous Motors (PMSM) and Permanent Magnet Linear Motors (PMLM). Dr. Riaz’s major contributions include pioneering adaptive PD-type iterative learning control (ILC) to mitigate friction uncertainty at low speeds, and designing robust sliding mode controllers (SMC) that guarantee convergence within a predefined time, even under physical constraints like control saturation and external disturbances. He has also advanced fault-tolerant control for multi-input multi-output (MIMO) systems and resilient control for cyber-physical robotic systems facing malicious threats. His most cited work, “A novel adaptive PD-type iterative learning control of the PMSM servo system” (2023), has garnered 29 citations, reflecting its impact on high-precision robotic applications. With over 80 total citations across his publications, Dr. Riaz is recognized for integrating extreme learning machines (ELM) with predefined-time control to achieve complete tracking in nonlinear systems. His research is instrumental in pushing the boundaries of modern control theory for next-generation intelligent robotics.
Research Focus
Key Achievements
Top Papers
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- 6A Novel Predefined Time PD-Type ILC Paradigm for Nonlinear Systems6 citations · 2022
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