Adaptive PID Control of UAV Altitude Dynamics Based on Parameter Optimization with Fuzzy Inference

Sarhan, Amr; Beijing, China Astronautics; Qin, Shiyin

doi:10.7763/ijmo.2016.v6.534

Cited by 18 publications

(7 citation statements)

References 26 publications

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“…In Sarhan and Qin's research study, the adaptive PID control of UAV altitude dynamics is built on parameter optimization with fuzzy inference. Likewise, the proposed adaptive PID control is a combination of traditional PID and fuzzy logic control schemes (Sarhan and Qin, 2016). A simple adaptive control scheme based on Model Reference Adaptive Systems (MRAS) algorithm is developed for the asymptotic output tracking problems with changing system parameters and disturbances under guaranteeing stability, in which the adaptive adjusting law is derived by using the Lyapunov Theory (Cuong et al, 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

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Adaptive and Neural Network Based Control of Pitch of Unmanned Aerial Vehicles

Matthews¹,

Yi²

2019

Journal of Mechatronics and Robotics

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The objective is to develop a new control strategy for quadrotor types of Unmanned Aerial Systems (UAVs) that assist in closing the existing research gaps between the undesired uncertainties and current control systems. This research investigates a system modeling and the effectiveness of error elimination through adaptation in the change of the plant/system's output. The UAV is controlled via Wi-Fi using MATLAB. Proportional-Integral-Derivative (PID) controller, the two degrees of freedom Proportional-Integral-Derivative (2DOF-PID) controller and the model reference Neural Network (NN) controller are used for the aircraft pitch control. External disturbances and modeling errors can lead to the instability and unpredictable behaviors of a system. Even though the disturbances cannot be avoided the effects of such disturbances can be reduced significantly by neural network-based adaptive controllers for high-performance tracking in the presence of the disturbances. The robust control systems with the adaptive control will improve navigation performance and operation for military, reconnaissance and surveillance applications. The application of a robust controller determines the performance by observing the system's behavior to identify and reduce the unpredictable effects of disturbances and achieve a better behavior of the whole system. By observing the system, the neural network is suitably used to learn from a set of training patterns. System identification uses a neural network to capture the behavior of system dynamics, which assists the neural network to train itself to act as a controller. Moreover, the system's performance of the controllers was tested using simulations to demonstrate the effectiveness in improving the speed and stability for the dynamic system. The presented work will produce a flexible, robust and effective control system model that provides additional stability and reduce the effects of disturbances.

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Section: Literature Reviewmentioning

confidence: 99%

“…   angular velocities (p,q,r) and inertial positions (pN,pE,h). Consequently, the model depends on external forces (fx,fy,fz) and moments (l,m,n) (Sarhan and Qin, 2016). The applications of this specific drone system provide intelligence, surveillance, reconnaissance, communications relay and other applications in a single flight.…”

Section: Introductionmentioning

confidence: 99%

Adaptive and Neural Network Based Control of Pitch of Unmanned Aerial Vehicles

Matthews¹,

Yi²

2019

Journal of Mechatronics and Robotics

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“…The second controller adjusts the position controller gains to reduce its action as the height error. Note that the features added by the second fuzzy gain scheduler, where the altitude error is used to control the position performance, are not present in other implementations [ 16 , 29 , 32 , 37 , 40 , 41 , 42 , 43 , 44 , 45 ]. This fuzzy controller allows changing performance following the UAV application requirements.…”

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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“…Due to the characteristics of mature control technology, simple principle, and easy implementation, the PID control method is widely applied in many elds [6]. However, the problems of real-time parameter adjustment and high-precision control in PID control are still the research hotspots and di culties of UAVs [19][20][21][22]. Yu and Yang [23] proposed an attitude control method of UAVs based on improved dual closedloop PID to optimize the ight result and improve its antijamming.…”

Section: Introductionmentioning

confidence: 99%

EKF-AF PID-Based Attitude Control Algorithm for UAVs

Song

Jiang

et al. 2022

Mobile Information Systems

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Aiming at the problems of manual setting of control rules and low control accuracy of the fuzzy PID control method, an attitude control method of the unmanned aerial vehicle (UAV) based on the extended Kalman filter (EKF) and adaptive fuzzy PID (AF PID) is proposed. The dynamics equations and measurement equations of UAV are established, and the EKF is used to estimate and predict attitude changes; an adaptive fuzzy PID control algorithm is designed, and the adaptive adjustment method is adopted to revise fuzzy control rules and parameters online; a simulation platform of the attitude control system (ACS) of UAV is built to simulate and verify the control effect of the method. The simulation results show that the proposed algorithm can estimate the change of attitude angles accurately and improve the control accuracy; meanwhile, it also ensures the stability and rapidity of the attitude changes. The research results can resolve the contradiction between high precision and rapid stability of complex systems to a certain extent.

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Adaptive PID Control of UAV Altitude Dynamics Based on Parameter Optimization with Fuzzy Inference

Cited by 18 publications

References 26 publications

Adaptive and Neural Network Based Control of Pitch of Unmanned Aerial Vehicles

Adaptive and Neural Network Based Control of Pitch of Unmanned Aerial Vehicles

Fuzzy Gain-Scheduling PID for UAV Position and Altitude Controllers

EKF-AF PID-Based Attitude Control Algorithm for UAVs

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