PID Optimization of Motion Servo Control System Based on Improved Artificial Fish Swarm Algorithm

Gao, Xiaoyu; Shi, Daosheng; Mu, Zixin

doi:10.1109/cac.2018.8623095

Cited by 3 publications

(2 citation statements)

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“…Hence, to get the optimal parameters, an optimization-based method is needed. Research studies on tuning PID controllers by metaheuristic methods cover particle swarm optimization (Kıyak, 2016), ant colony algorithm (Sahoo and Panda, 2018), genetic algorithm (Yazgan et al , 2019), fish swarm algorithm (Gao and Shi, 2018), stochastic search algorithm (Sivalingam et al , 2017) and grey-wolf optimization algorithm (Sahoo and Panda, 2018).…”

Section: Introductionmentioning

confidence: 99%

Improved PID rate control of a quadrotor with a convexity-based surrogated model

Kaba

2021

AEAT

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Purpose The purpose of this paper is to develop, extend and propose an improved proportional integral derivative (PID) rate control of a quadrotor unmanned aerial vehicle based on a convexity-based surrogated firefly algorithm. Design/methodology/approach An improved PID controller structure is proposed for the rate dynamics of the quadrotor. Optimality of the controller is ensured by a recent, simple yet efficient firefly optimization method. The hybrid structure is further enhanced with a convexity-based surrogated model function. Findings Monte Carlo, transient response, error metrics and histogram distribution analyzes are conducted to show the performance of the proposed controller. The performance of the proposed method is evaluated under various convex combination values to further investigate the effect of the proposed surrogated model. According to the results, the proposed method is capable of controlling the rate quadrotor dynamics with the steady-state error of 0.0023 (rad/s) for P, −0.0024 (rad/s) for Q and 0 (rad/s) for the R state, respectively. Also, the least mean objective value is achieved at = 0 value of convexity in Monte Carlo trials. Originality/value The originality of this paper is to propose an improved PID rate controller with a convexity-based surrogated firefly algorithm.

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Section: Introductionmentioning

confidence: 99%

Improved PID rate control of a quadrotor with a convexity-based surrogated model

Kaba

2021

AEAT

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“…Researchers have improved the fish school algorithm because of considering the limitations in its use. Gao et al 9 adopts the method of mutation factor to obtain adaptive step size to improve the global optimization ability of fish school algorithm. He et al 10 analyzes the key optimization steps of the artificial fish school algorithm, and uses the adaptive step size method to improve the global optimization ability.…”

Section: Introductionmentioning

confidence: 99%

A lifetime optimization method of new energy storage module based on new artificial fish swarm algorithm

Zhi

Wang

et al. 2021

Advances in Mechanical Engineering

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The demand for new energy will continue to expand as the environment changes and fossil energy decreases. However, the instability of new energy has slowed down the development of new energy. The joint use of new energy and energy storage modules effectively solves the shortcomings of new energy. The article proposed a lifetime optimization method of new energy storage module based on new artificial fish swarm algorithm. Firstly the life model based on the battery capacity [Formula: see text], charging current [Formula: see text], and discharge current [Formula: see text] is built. Secondly, the deep learning method is used to improve the step length and speed change of artificial fish-school algorithm. Finally, the simulation platform detects the optimized parameters [Formula: see text]. The simulation results show that optimized parameters can help extend the life of the energy storage module.

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Research on PID Position Control of a Hydraulic Servo System Based on Kalman Genetic Optimization

et al. 2022

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With the wide application of hydraulic servo technology in control systems, the requirement of hydraulic servo position control performance is greater and greater. In order to solve the problems of slow response, poor precision, and weak anti-interference ability in hydraulic servo position controls, a Kalman genetic optimization PID controller is designed. Firstly, aiming at the nonlinear problems such as internal leakage and oil compressibility in the hydraulic servo system, the mathematical model of the hydraulic servo system is established. By analyzing the working characteristics of the servo valve and hydraulic cylinder in the hydraulic servo system, the parameters in the mathematical model are determined. Secondly, a genetic algorithm is used to search the optimal proportional integral differential (PID) controller gain of the hydraulic servo system to realize the accurate control of valve-controlled hydraulic cylinder displacement in the hydraulic servo system. Under the positioning benchmark of step signal and sine wave signal, the PID algorithm and the genetic optimized PID algorithm are compared in the system simulation model established by Simulink. Finally, to solve the amplitude fluctuations caused by the GA optimized PID and reduce the influence of external disturbances, a Kalman filtering algorithm is added to the hydraulic servo system to reduce the amplitude fluctuations and the influence of external disturbances on the system. The simulation results show that the designed Kalman genetic optimization PID controller can be better applied to the position control of the hydraulic servo system. Compared with the traditional PID control algorithm, the PID algorithm optimized by genetic algorithm improves the system’s response speed and control accuracy; the Kalman filter is a good solution for the amplitude fluctuations caused by GA-optimized PID that reduces the influence of external disturbances on the hydraulic servo system.

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PID Optimization of Motion Servo Control System Based on Improved Artificial Fish Swarm Algorithm

Cited by 3 publications

References 0 publications

Improved PID rate control of a quadrotor with a convexity-based surrogated model

Improved PID rate control of a quadrotor with a convexity-based surrogated model

A lifetime optimization method of new energy storage module based on new artificial fish swarm algorithm

Research on PID Position Control of a Hydraulic Servo System Based on Kalman Genetic Optimization

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