Constrained particle swarm optimization for health maintenance in three-mass resonant servo control system with LuGre friction model

Hung, Yao-Min; Lee, Chia-Yen; Tsai, Ching-Hsiung; Lu, Yen-Ming

doi:10.1007/s10479-021-04255-1

Cited by 6 publications

(3 citation statements)

References 35 publications

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“…With the surge in workload, some scholars have turned to purely algorithmic improvements to improve the universality of identification technology. Hung et al [57] used constrained particle swarm optimization (CPSO) to estimate the parameters of a three-mass resonant servo control Chen et al [54] proposed a parameter identification method based on the fireworks algorithm (FWA). According to the experimental data of the damper dynamic characteristics and the simulation data of the Bouc-Wen model, the FWA was used to identify the seven parameters of the Bouc-Wen model.…”

Section: Metaheuristicsmentioning

confidence: 99%

“…With the surge in workload, some scholars have turned to purely algorithmic improvements to improve the universality of identification technology. Hung et al [57] used constrained particle swarm optimization (CPSO) to estimate the parameters of a three-mass resonant servo control system based on the LuGre friction model to allow machines to operate simultaneously. Through parameter estimation and feedforward compensation of the model, the main effect of current variation in the mechanical system was successfully reflected.…”

Section: Metaheuristicsmentioning

confidence: 99%

“…Particle Swarm Optimization (PSO) [45,57] Memory, fast search speed, few parameters, simple structure, etc.…”

Section: Metaheuristicsmentioning

confidence: 99%

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A Review of Key Technologies for Friction Nonlinearity in an Electro-Hydraulic Servo System

et al. 2022

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In a high-precision servo system, the nonlinear friction link is the key factor affecting the system performance. Reasonable solving of the friction link in servo systems has become a focus of current research. This paper summarizes the friction nonlinearity that affects the control performance of servo systems. First, the characteristics of friction are summarized, and the advantages and disadvantages of typical friction models in recent years are analyzed. Subsequently, existing friction model parameter identification methods are introduced and evaluated. On this basis, the development level of the friction nonlinear control strategy is analyzed from three aspects: friction model-based control, friction model-free control, and compound control. Finally, the objective advantages and disadvantages of the existing technology are summarized, and the future development direction of the friction model and selection reference for the nonlinear friction control strategy are comprehensively discussed.

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Section: Metaheuristicsmentioning

confidence: 99%

“…With the surge in workload, some scholars have turned to purely algorithmic improvements to improve the universality of identification technology. Hung et al [57] used constrained particle swarm optimization (CPSO) to estimate the parameters of a three-mass resonant servo control system based on the LuGre friction model to allow machines to operate simultaneously. Through parameter estimation and feedforward compensation of the model, the main effect of current variation in the mechanical system was successfully reflected.…”

Section: Metaheuristicsmentioning

confidence: 99%