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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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    Modern artificial intelligence technics for unmanned aerial vehicles path planning and control
    (2025) Zamoum, Yasmine; Baiche, Karim; Benkeddad, Youcef; Bouzida, Brahim
    Unmanned aerial vehicles (UAVs) require effective path planning algorithms to navigate through complex environments. This study investigates the application of Deep Q-learning and Dyna Q-learning methods for UAV path planning and incorporates fuzzy logic for enhanced control. Deep Q-learning, a reinforcement learning technique, employs a deep neural network to approximate Q-values, allowing the UAV to improve its path planning capabilities by maximizing cumulative rewards. Conversely, Dyna Q-learning leverages simulated scenarios to update Q- values, refining the UAV’s decision-making process and adaptability to dynamic environments. Additionally, fuzzy logic control is integrated to manage UAV movements along the planned path. This control system uses linguistic variables and fuzzy rules to handle uncertainties and imprecise information, enabling real-time adjustments to speed, altitude, and heading for accurate path following and obstacle avoidance. The research evaluates the effectiveness of these methods individually, with a focus on model-free learning in a gradual training approach, and compares their performance in terms of path planning accuracy, adaptability, and obstacle avoidance. The paper contributes to a deeper understanding of UAV path planning techniques and their practical applications in various scenarios.
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    Optimal control design for uncertain aerial manipulator system based on an adaptive approach
    (Inderscience Publishers, 2024) Riache, Samah; Kidouche, Madjid; Doghmane, Mohamed Zinelabidine; Tee, Kong Fah
    In this paper, an optimal controller has been proposed for an aerial manipulator (AM) consisting of a quadrotor uncertain system with a two-degrees-of-freedom robotic arm. Wherein, the dynamics of this system have been derived based on Gauss's principle. The employment of this principal has permitted the pinpoint of the inner structure of the uncertain system and its possible moves. It has kept the AM in a very precise formation to analyse its dynamics and propose the suitable control. The proposed controller is designed using an adaptive approach of the non-singular terminal sliding mode technique. The main contribution is that the proposed approach guarantees both the good tracking of the desired trajectories in finite time and the chattering effect attenuation without overestimating the switching control gains. The design does not necessitate a priori knowledge of the upper limits of disturbances; the stability of the system has been established through the utilisation of Lyapunov theory. The simulation results have proved the effectiveness and robustness of the proposed optimal nonlinear terminal sliding mode technique for such an uncertain system in comparison to the sliding mode controller.
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    Modeling and control of UAV quadrotor
    (IEEE, 2019) Zammoum Boushaki, Razika; Aribi, Yacine; Loubar, Hocine; Hamza, Younes; Kouzou, Abdellah
    Since the advances in technologies and the ability to manufacture miniature sensors and controllers using the Micro-Electro-Mechanical Systems (MEMS) technologies, there have been a lot of advances in the Unmanned Aerial Vehicle (UAV) area. A lot of the research conducted focused on the quadrotor due to its previously mentioned advantages of easier manufacturing, compactness and maneuverability among others. a detailed description of the dynamic modelling of the Quadrotor is presented. Various control strategies like the Proportional Derivative Control, the Sliding Mode Control and the Backstepping Control methods have been elucidated and implemented in MATLAB and SIMULINK. Simulations have been carried out and the results have been presented