Fuzzy PID controller for UAV tracking moving target

Si, Weiyong; She, Haoping; Wang, Zhanglong

doi:10.1109/ccdc.2017.7979027

Cited by 18 publications

(5 citation statements)

References 6 publications

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“…To improve steady-state performance for low values of e, K p and Ki should both be increased. Ki should be kept moderate to prevent oscillations around the corresponding static values and to take the system's anti-interference capability into account [10]. The two vehicles' paths are displayed in the figure 6.…”

Section: The Adjustment Rule Of Pid Controlmentioning

confidence: 99%

An improved braitenberg policy for object tracking

2024

ACE

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In vision-based unmanned vehicle tracking of moving vehicles, it is difficult to achieve accurate tracking of motorized vehicles with conventional fixed-parameter PID controllers due to their varying speeds and changes over time. To generate control signals for the systems and move the controlled object, the PID controller performs calculations based on percentage, integral, and derivative data. This is the discrete PID controller method. In this paper an improved tracking method, the Braitenberg policy, is proposed and a series of experiments are conducted in Simulink. The experimental results verify that the method can achieve stable tracking in simple environment for both general and extreme cases. The method can be used for future experiments for both general and extreme cases. The method can be used for future experiments in more scenarios. Braitenberg policy provides stronger and more stable tracking performance in extreme cases compared to conventional PID control. The algorithm provides more opportunities for tracking research and a small idea for the integration of multiple algorithms.

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Section: The Adjustment Rule Of Pid Controlmentioning

confidence: 99%

An improved braitenberg policy for object tracking

2024

ACE

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“…In the past few years, researchers worldwide have proposed many controllers for UAVs. As a classical controller, proportional integral derivative (PID) controllers have been widely used recently [9][10][11]. However, PID parameter tuning is difficult and it is sensitive to disturbances.…”

Section: Related Workmentioning

confidence: 99%

“…where β 1 , β 2 , β 3 are observer gains. Then, the output variables of the extended state observer (11) can track the state variables of system (10), namely,…”

Section: Tracking Differentiatormentioning

confidence: 99%

Design of the UAV Trajectory Tracking System Based on the Adaptive Neural Network - ADRC Method

Cai

Zhu

Yao

2022

Wireless Communications and Mobile Computing

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In order to solve the problems of internal uncertainty and external disturbance of an unmanned aerial vehicle (UAV), this paper proposes the active disturbance rejection controller (ADRC) based on the adaptive radial basis function (RBF) neural network. Firstly, the dynamic model of a quadrotor with disturbance is analyzed and the controller is designed based on the ADRC method. Secondly, the RBF network is used to estimate the unknown parameter b of the system and the Lyapunov function is constructed to prove the stability of the closed-loop system. Finally, simulation results of a spiral ascent and a climbing maneuver flight illustrate that the controller proposed in this paper has high tracking accuracy and strong robustness under different flight scenarios; the average tracking error of the outdoor flight experiment is about 0.22 m, which further verifies the effectiveness of the proposed controller.

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“…Researchers around the world have designed many control methods for quadrotor flight control. Proportional integral derivative (PID) method is a common strategy in practical engineering and has been used for UAV control (Goodarzi et al , 2013; Si et al , 2017). However, PID control algorithm cannot effectively solve problems such as external disturbances and actuator failures.…”

Section: Introductionmentioning

confidence: 99%

FTESO-adaptive neural network based safety control for a quadrotor UAV under multiple disturbances: algorithm and experiments

Cai,

Zhu,

Yao

2024

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Purpose Quadrotors have been applied in various fields. However, because the quadrotor is subject to multiple disturbances, consisting of external disturbances, actuator faults and parameter uncertainties, it is difficult to control the unmanned aerial vehicle (UAV) to achieve high-precision tracking performance. This paper aims to design a safety controller that uses observer and neural network method to improve the tracking performance of UAV under multiple disturbances. The experiments prove that this method is effective. Design/methodology/approach First, to actively estimate and compensate the synthetic uncertainties of the system, a finite-time extended state observer is investigated, and the disturbances are transformed into the extended state of the system for estimation. Second, an adaptive neural network controller that does not accurately require the dynamic model knowledge is designed based on the estimated value, where the weights of the neural network can be dynamically adjusted by the adaptive law. Furthermore, the finite-time bounded convergence of the proposed observer and the stability of the system are proved through homogeneous theory and Lyapunov method. Findings The figure-“8” climbing flight simulation and real flight experiments illustrate that the proposed safety control strategy has good tracking performance. Originality/value This paper proposes the safety control structure of the UAV, which combines the extended state observer with the neural network method. Numerical simulation results and actual flight experiments demonstrate the effectiveness of the proposed control strategy.

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Fuzzy PID controller for UAV tracking moving target

Cited by 18 publications

References 6 publications

An improved braitenberg policy for object tracking

An improved braitenberg policy for object tracking

Design of the UAV Trajectory Tracking System Based on the Adaptive Neural Network - ADRC Method

FTESO-adaptive neural network based safety control for a quadrotor UAV under multiple disturbances: algorithm and experiments

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