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About

Lotfi Boussaid is a researcher specializing in intelligent control systems and mobile robot navigation, with a focus on applying nature-inspired optimization algorithms to enhance autonomous decision-making. His work centers on developing adaptive neurofuzzy controllers and PID controllers optimized through metaheuristic techniques, enabling robots to navigate complex, dynamic, and unfamiliar environments with improved accuracy and stability. Boussaid’s most cited paper, “TLBO-Based Adaptive Neurofuzzy Controller for Mobile Robot Navigation in a Strange Environment” (2018, 32 citations), pioneers the use of Teaching-Learning-Based Optimization to train ANFIS structures for optimal trajectory planning. His follow-up work, “Same Fuzzy Logic Controller for Two-Wheeled Mobile Robot Navigation in Strange Environments” (2019, 31 citations), further advances fuzzy logic control for smooth navigation. Additionally, his 2017 study on PSO-tuned PID controllers (14 citations) demonstrates robust obstacle avoidance and path efficiency in complex dynamic settings. Collectively, Boussaid’s contributions bridge computational intelligence and robotics, offering scalable solutions for real-world autonomous systems. His research is highly relevant for students and engineers working on intelligent robotics, control theory, and optimization-driven navigation.