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About

Ali Mallem is a researcher whose work spans advanced robotics control systems and biomedical signal acquisition, with particular expertise in mobile robot trajectory tracking and intelligent control methodologies. His most significant contributions lie in developing sophisticated control strategies for wheeled mobile robots — systems notoriously challenging to model due to their nonlinear and nonholonomic properties. Mallem has pioneered hybrid approaches that combine classical and modern techniques, including PID fast terminal sliding mode dynamic inverse control, fuzzy sliding mode control, and neural network-enhanced global fast sliding mode strategies. His most cited work (2016, 11 citations) introduced a PID-based fast terminal sliding mode controller that substantially improved trajectory tracking accuracy under real-world conditions. Subsequent research explored the integration of fuzzy logic, radial basis function (RBF) networks, and neural networks to enhance robustness against disturbances — a persistent challenge in mobile robotics. Beyond robotics, Mallem has extended his expertise into biomedical engineering, developing a versatile bio-potential measurement system capable of acquiring EEG, ECG, and EMG signals with high precision. With a growing citation record across multiple domains, his interdisciplinary contributions make him a noteworthy figure in applied control systems research.