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About

Wafa Boukadida is a control systems researcher whose work sits at the intersection of robust control theory, optimization, and robotics. Her research focuses primarily on sliding mode control (SMC), trajectory tracking, and the application of evolutionary optimization techniques — particularly genetic algorithms — to complex nonlinear robotic systems. Boukadida has made notable contributions to the design of optimal control frameworks for robotic manipulators, addressing the persistent challenges of nonlinearities, unmodeled dynamics, and system uncertainties that characterize real-world robotic applications. Her most recognized work, published in 2018 and accumulating 22 citations, introduced a multi-objective genetic algorithm-based approach to optimize sliding mode control laws for SCARA robots, representing a significant methodological advance in discrete robotic control. Building on this foundation, her subsequent research further refined optimal discrete sliding mode strategies by integrating Linear Quadratic Regulator principles with first-order SMC, producing robust and computationally efficient solutions for MIMO systems. Collectively, her publications have garnered over 40 citations, reflecting meaningful influence within the robotics and control engineering communities. Her body of work offers valuable tools for researchers and engineers seeking reliable, optimized control solutions for industrial robotic systems operating under uncertain conditions.