Quadrotor UAV attitude stabilization using fuzzy robust control

Alabazares, David Lara; Rabhi, Abdessalem; Pégard, C.; Garcia, Fernando Torres; Romero, Gerardo

doi:10.1177/01423312211002588

Cited by 19 publications

(10 citation statements)

References 47 publications

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“…Furthermore, it cannot ensure closed-loop performance in different conditions of flight. Fuzzy controllers applied to UAVs undoubtedly have some advantages in performance evaluation compared to PID controllers, such as dynamic property and steady-state error [ 21 ]. In the work of [ 22 ], the authors used the Takagi–Sugeno (TS) technique, along with linear parallel distributed compensation for vehicle stabilization.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Gain-Scheduling PID for UAV Position and Altitude Controllers

Melo

Andrade

Guedes

et al. 2022

Sensors

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Unmanned aerial vehicle (UAV) applications have evolved to a wide range of fields in the last decade. One of the main challenges in autonomous tasks is the UAV stability during maneuvers. Thus, attitude and position control play a crucial role in stabilizing the vehicle in the desired orientation and path. Many control techniques have been developed for this. However, proportional integral derivative (PID) controllers are often used due their structure and efficiency. Despite PID’s good performance, different requirements may be present at different mission stages. The main contribution of this research work is the development of a novel strategy based on a fuzzy-gain scheduling mechanism to adjust the PID controller to stabilize both position and altitude. This control strategy must be effective, simple, and robust to uncertainties and external disturbances. The Robot Operating System (ROS) integrates the proposed system and the flight control unit. The obtained results showed that the proposed approach was successfully applied to the trajectory tracking and revealed a good performance compared to conventional PID and in the presence of noises. In the tests, the position controller was only affected when the altitude error was higher, with an error of 2% lower.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Gain-Scheduling PID for UAV Position and Altitude Controllers

Melo

Andrade

Guedes

et al. 2022

Sensors

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“…O estado da arte em Veículos Aéreos Não Tripulados (VANTS), Unmanned Aerial Vehicle (UAV), tem recebido diversas contribuições e avanços em pesquisas nos últimos anos [1,2,3,4,5,6]. Tais avanços têm sido motivados, principalmente, pelos desenvolvimentos em tecnologia de sensoriamento, armazenamento de energia de alta densidade e processamento de dados [7].…”

Section: Introductionunclassified

“…Dentre as técnicas de controle aplicadas, pode-se destacar as seguintes: a abordagem de Lyapunov [12], a qual garante, sob certas condições, a estabilidade assintótica do quadricóptero; a estrutura de realimentação PD, Proporcional-Derivativo [13,14], com propriedade de convergência exponencial devido à compensação dos termos Coriolis e giroscópicos, e a estrutura PID, Proporcional-Integral-Derivativo [7,15], a qual não requer o conhecimento de parâmetros específicos do modelo e a lei de controle é muito mais fácil de implementar, porém com robustez limitada contra perturbações; o controle RLQ (Regulador Linear Quadrático) [16], e controle H ∞ [4], cuja vantagem é que exibem boas propriedades de robustez: margem de ganho infinitamente crescente, margem de fase entre ±60 • e boa tolerância à nãolinearidades; controle adaptativo [17,18,19], que fornecem bom desempenho com parâmetros incertos e dinâmicas não modeladas. Existem outros algoritmos de controle para melhorias do desempenho de sistemas quadrirrotores, tais como técnicas fuzzy [1,20], redes neurais [21], controle backstepping [22,23], controle baseado em realimentação visual [24,25], e controle baseado em aprendizagem por reforço [26,27,28]. Em [29] são destacados vários projetos de melhoria de estabilidade para quadricópteros, que são capazes de realizar voos autônomos apenas com o uso de sensores a bordo para estimação da atitude, altitude, posição horizontal e voos trans-lacionais.…”

Section: Introductionunclassified

Optimal Control and Adaptive Learning for Stabilization of a Quadrotor-type Unmanned Aerial Vehicle via Approximate Dynamic Programming

Souza

Rêgo

Sousa

et al. 2022

RITA

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The development of an optimal controller for stabilization of a quadrotor system using an adaptive critic structure based on policy iteration schemes is proposed in this paper. This approach is inserted in the context of Approximate Dynamic Programming and it is used to solve optimal decision problems on-line, without requiring complete knowledge of the system dynamics model to be controlled. The main feature of the adaptive critic design method that allows for on-line implementation is that it solves the Bellman optimality equation in a forward-in-time fashion, whereas traditional dynamic programming requires a backward-in-time procedure. This feedback control design technique is able to tune the controller parameters on-line in the presence of variations in plant dynamics and external disturbances using data measured along the system trajectories. Computational simulation results based on a quadrotor model demonstrate the effectiveness of the proposed control scheme.

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“…In some extreme cases, the USV attitude dynamic model may be completely unknown, and thus these model-based controllers cannot be directly implemented. The intelligent control is another efficient approach for the attitude control of USV by adopting the neural networks (NNs) or fuzzy logic systems to directly construct the controllers or embedding the NNs or fuzzy logic systems into the baseline controllers (Alabazares et al, 2021; Guan et al, 2005; Hu and Xiao, 2012; Leeghim et al, 2009; Yang and Yan, 2016a, 2016b; Yao et al, 2022a, 2022b; Zou, 2016; Zou et al, 2011). Owing to the powerful universal approximation ability of NNs and fuzzy logic systems, the intelligent control is model-free and does not require any prior knowledge on the system model.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural fault-tolerant control for output-constrained attitude tracking of unmanned space vehicles

Yao

Jahanshahi

Golestani

2022

Transactions of the Institute of Measurement and Control

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The challenging problem of attitude tracking control for unmanned space vehicles (USVs) subject to actuator faults and output constraints is addressed in this study. A novel adaptive neural fault-tolerant controller is proposed by integrating the neural networks (NNs) and barrier Lyapunov function (BLF) with the backstepping technique. Two NNs are adopted to approximate the uncertain nonlinear terms caused by unknown attitude dynamics and actuator faults, respectively. Moreover, the BLF is introduced to tackle the output constraints. It is strictly proved that all the closed-loop error signals are uniformly ultimately bounded under the proposed controller. Totally, the proposed adaptive neural fault-tolerant controller has the following two distinctive features. (1) The proposed controller is model-free and can still be applicable even when the USV attitude dynamic model is completely unknown in advance. (2) The proposed controller can guarantee the attitude tracking error always within the predefined output constraints even in the presence of actuator faults and thus ensuring safety. Finally, the excellent tracking performance of the proposed controller is verified through numerical simulations and comparisons.

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Quadrotor UAV attitude stabilization using fuzzy robust control

Cited by 19 publications

References 47 publications

Fuzzy Gain-Scheduling PID for UAV Position and Altitude Controllers

Fuzzy Gain-Scheduling PID for UAV Position and Altitude Controllers

Optimal Control and Adaptive Learning for Stabilization of a Quadrotor-type Unmanned Aerial Vehicle via Approximate Dynamic Programming

Adaptive neural fault-tolerant control for output-constrained attitude tracking of unmanned space vehicles

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