Designing of New Global Sliding Mode Controller for Bridge Crane

Wang, Tianlei; Tan, Ning; Qiu, Jiongzhi

doi:10.1145/3452940.3452942

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…However, both neural network control and fuzzy control need to obtain the sample database through extensive experiments ahead of schedule to adjust the parameters of the controller, which is very complicated, so scholars put forward the swarm intelligence algorithm. Wang Tianlei et al 9 proposed a cuckoo search algorithm with adaptive selection of crossover operator to set the parameters of the controller and achieve optimal control of the crane control system.Yang Shipin et al 10 designed a new antisense RNA membrane computation (ARNA-MC) optimization algorithm and applied it to the parameter estimation of optimal proportional Integral differential (PID) controller and optimal sliding mode controller respectively to achieve accurate positioning and fast anti-swing.…”

Section: Introductionmentioning

confidence: 99%

Design of bridge crane sliding mode controller based on improved grey wolf algorithm

Hao

Liu²,

Li³

et al. 2022

6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022)

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To solve the complicated parameter tunning problem of bridge crane sliding mode controller, a sliding mode controller based on IGWO (Improved Grey Wolf Algorithm) was designed. Different from the existing methods, the proposed method can obtain good control effect without going through the complicated manual adjustment process. Specifically, a new switching function is designed to reduce chattering. In addition, the population diversity of traditional gray Wolf algorithms is poor. To enhance its search ability, an IGWO algorithm based on neighborhood learning is proposed and the controller parameters are set with it. The simulation consequences show that the proposed controller can position and anti-swing well.

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Section: Introductionmentioning

confidence: 99%