Attitude Control of Autonomous Underwater Vehicle Based on Improved Firefly PID Method

You, Lianggen; Liu, Mingyong; Shi, Tingchao; Zhang, Jiao; Niu, Yun

doi:10.1109/ieeeconf38699.2020.9389461

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…improper selection of controller parameters (You et al, 2020). Wang et al proposed an adaptive traceless Kalman filter (UKF) algorithm based on the Sage-Husa filter with robust estimation to achieve effective estimation and compensation of system noise, and proved that the algorithm can effectively improve the accuracy of underwater robot positioning by simulating long baseline and ultra-short baseline positioning experiments (Wang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The nonlinearity of the thrust vector caused by the thruster was overcome by the decomposition method, the control input was transferred to the system using a thrust compensator, and the effectiveness of the controller was verified by simulation and experiment (Bak et al, 2022). You et al, for the attitude control of an underwater robot, combined the improved firefly algorithm with the PID control method to achieve adaptive tuning of the PID control parameters, and the simulation results showed that the algorithm solved the problems of overshoot and long response time caused by improper selection of controller parameters (You et al, 2020). Wang et al proposed an adaptive traceless Kalman filter (UKF) algorithm based on the Sage‐Husa filter with robust estimation to achieve effective estimation and compensation of system noise, and proved that the algorithm can effectively improve the accuracy of underwater robot positioning by simulating long baseline and ultra‐short baseline positioning experiments (Wang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Finite‐time command filter control for dynamic positioning of remotely operated vehicles based on disturbance observer

Tang,

Qin,

Dang

2024

Journal of Field Robotics

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To deal with the problems of low positioning accuracy and poor stability caused by model parameter uncertainty and external disturbances in the remotely operated vehicle (ROV) dynamic positioning control system, the adaptive fuzzy control is combined with a disturbance observer to estimate the lumped disturbances and the error compensation mechanism is introduced to design the ROV dynamic positioning controller based on the finite‐time command filter. The combination of finite‐time command filter control and the backstepping method ensures that the positioning error converges to a smaller neighborhood near zero in finite time. The stability of the closed‐loop system is demonstrated according to the Lyapunov stability theory. The simulation results show that a 13% performance improvement over traditional backstepping methods is achieved by the designed controller, effectively suppressing system lumped disturbances and achieving fast and accurate positioning of the desired positions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Finite‐time command filter control for dynamic positioning of remotely operated vehicles based on disturbance observer

Tang,

Qin,

Dang

2024

Journal of Field Robotics

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Parameter optimization of PID controller for water and fertilizer control system based on partial attraction adaptive firefly algorithm

Huang

Tian

Liu

et al. 2022

Sci Rep

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Proportional Integral Derivative (PID) control is the main control method in the process of agricultural water and fertilizer regulation, and its parameter setting directly affects the control effect of water and fertilizer regulation. However, the traditional PID parameters are adjusted manually such as using the critical proportionality method, which is time-consuming and difficult to achieve optimal control effects. To solve the optimal combination of PID control parameters and improve the control effect of water and fertilizer regulation, a partial attraction adaptive firefly algorithm (PAAFA) is proposed in this paper. Specifically, a partial attraction strategy is designed to speed up the convergence of the PAAFA and reduce the oscillation problem at the late stage of the algorithm. In addition, an adaptive inertia weight operator is proposed to balance the global search capability and local search capability of PAAFA and avoid the algorithm from trapping in the local optimum. Subsequently, to test the performance of PAAFA, the algorithm is subjected to a series of simulation experiments and bench tests with the latest methods, i.e., genetic algorithm (GA), Adaptive genetic algorithm (AGA), and firefly algorithm (FA) applied to PID parameter optimization problems. The simulation results demonstrate that the regulation times of the response curve of PAAFA-based PID control are reduced by 22.75%,10.10%and 20.61%, respectively, compared with GA, AGA, and FA. The bench test results show that the PAAFA-based PID control has the smallest relative error, and best control accuracy compared to GA, AGA, and FA, with an average relative error reduction of 3.99, 2.42, and 3.50 percentage points respectively.

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Attitude Control of Autonomous Underwater Vehicle Based on Improved Firefly PID Method

Cited by 2 publications

References 3 publications

Finite‐time command filter control for dynamic positioning of remotely operated vehicles based on disturbance observer

Finite‐time command filter control for dynamic positioning of remotely operated vehicles based on disturbance observer

Parameter optimization of PID controller for water and fertilizer control system based on partial attraction adaptive firefly algorithm

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