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About

Belkacem Bekhiti is a control systems researcher whose work focuses on intelligent, adaptive control strategies for complex robotic systems, including bipedal walking robots, quadrotor UAVs, and self-balancing autonomous platforms. His most cited paper (2025, 6 citations) introduces a neural adaptive nonlinear MIMO control framework that enhances bipedal locomotion stability in hazardous environments by combining nonlinear dynamic inversion, finite-time convergence, and radial basis function (RBF) neural networks. This work addresses critical challenges in uncertain, real-world applications. Bekhiti also developed an intelligent spectral factor relocation method for quadrotor UAVs (2017, 2 citations), enabling precise eigenstructure assignment to achieve desired latent dynamics. More recently, he proposed a neuro-fuzzy adaptive MIMO control scheme for two-wheeled robots (2025, 1 citation), which integrates maximum likelihood identification with ANFIS-based predictive control via recursive solution of matrix Diophantine equations. His contributions lie at the intersection of nonlinear control theory, neural networks, and fuzzy systems, offering robust, adaptive solutions for autonomous systems operating under uncertainty. With a growing citation record, Bekhiti’s work is increasingly recognized for advancing intelligent control in robotics and aerospace applications.