Publications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13

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Author: search.filters.author.Boushaki Zamoum, Razika

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    Linear and nonlinear control design for a quadrotor
    (2025) Hadid, Samira; Boushaki Zamoum, Razika; Refis, Youcef
    In the current study, the quadrotor's nonlinear dynamic model is developed using the Newton-Euler approach. Following that, several nonlinear and linear control strategies for tracking the quadrotor's trajectory are applied. First, by employing distinct controllers for each output variable, direct application of the linear proportional integral derivative (PID) controller to the nonlinear system is realized. This system may also be linearized about an operational point to generate linear controllers, according to the linear quadratic regulator (LQR) demonstration. Nevertheless, in practice, the system dynamics may not always be accurately reflected by this linear approximation and may even be relatively wasteful. Nonlinear regulators, including the feedback linearization (FBL) controller, sliding mode controller (SMC), and modified sliding mode controller (MSMC), perform better in such situations. The trajectory tracking capabilities, dynamic performance, and potential disruption impact of both methods are evaluated and compared. The FBL with LQR was the best controller among them all. The SMC and the MSMC were also very good in tracking the trajectory.
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    Layered task planning for mobile manipulators
    (Elsevier, 2019) Djezairi, Salim; Boushaki Zamoum, Razika; Akli, Isma; Bouzouia, Brahim
    Task planning for mobile manipulators is a challenging problem in robotics. Mobile ma- nipulators can perform a wide range of tasks in complex environments. They must process the symbolic knowledge in order to find a sequence of actions that achieves the specified goals. This paper proposes a hybrid approach named Planning with Shortcuts. It uses a data structure called Layered Task Graph (LTG) which groups primitive actions, robot skills and task patterns in a strictly hierarchical manner. It then uses the LTG to create shortcuts in the state space in order to accelerate the search. A task planner is called to find a set of actions that achieves the goal task. The result of the search is a sequence of high level actions and primitive actions. The high level actions are then decomposed using the task decomposition approach.