Particle swarm optimization based feedforward controller for a XY PZT positioning stage

Lin, Chao‐Nan; Lin, Po-Ting

doi:10.1016/j.mechatronics.2012.02.001

Cited by 40 publications

(16 citation statements)

References 49 publications

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“…The integration establishes the grid plane of the major and minor loops of hysteresis for the integral hysteresis model. Differential-type hysteresis models include Duhem model [19], Dahl model [20], and the Bouc-Wen model [15]. The differential hysteresis models use nonlinear differential equations to describe hysteresis dynamics.…”

Section: System Modelingmentioning

confidence: 99%

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Hysteresis modeling and tracking control for a dual pneumatic artificial muscle system using Prandtl–Ishlinskii model

Lin

Shen

et al. 2015

Mechatronics

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Section: System Modelingmentioning

confidence: 99%

“…12 shows the extracted hysteresis between the pressure and length, where the internal pressure of the PAM1 is at 4 bar. The extracted hysteresis force is captured by Bouc-Wen model, which was formulated as described in [20] as follows:…”

Section: Case Imentioning

confidence: 99%

Hysteresis modeling and tracking control for a dual pneumatic artificial muscle system using Prandtl–Ishlinskii model

Lin

Shen

et al. 2015

Mechatronics

Self Cite

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“…Using a subspace-based modelling technique as described in [16], the system is identified. The modelling methods such as neural networks or genetic algorithms [17,18] are typically used to model systems where all parameters are not directly identifiable. Typically, a simple second-order transfer function with a suitably low damping coefficient and the correct resonant frequency is sufficient to capture the dominant in-bandwidth dynamics of a nanopositioner's axis.…”

Section: Linear Dynamics Modelmentioning

confidence: 99%

High-Precision Control of a Piezo-Driven Nanopositioner Using Fuzzy Logic Controllers

Altaher

Aphale

2018

Computers

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This paper presents single-and dual-loop fuzzy control schemes to precisely control the piezo-driven nanopositioner in the x-and y-axis directions. Various issues are associated with this control problem, such as low stability margin due to the sharp resonant peak, nonlinear dynamics, parameter uncertainty, etc. As such, damping controllers are often utilised to damp the mechanical resonance of the nanopositioners. The Integral Resonant Controller (IRC) is used in this paper as a damping controller to damp the mechanical resonance. A further inherent problem is the hysteresis phenomenon (disturbance), which leads to degrading the positioning performance (accuracy) of the piezo-driven stage. The common approach to treat this disturbance is to invoke tracking controllers in a closed-loop feedback scheme in conjunction with the damping controllers. The traditional approach uses the Integral Controller (I) or Proportional Integral (PI) as a tracking controller, whereas this paper introduces the Proportional and Integral (PI)-like Fuzzy Logic Controller (FLC) as a tracking controller. The effectiveness of the proposed control schemes over conventional schemes is confirmed through comparative simulation studies, and results are presented. The stability boundaries of the proposed control schemes are determined in the same way as with a conventional controller. Robustness against variations in the resonant frequency of the proposed control schemes is verified.

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“…In other words, each bird knows which one is flying the best (closer to the optimum) and tries to follow it modifying its velocity (v x and v y ) considering the distance (XY difference) between itself (pbest) and the best bird (gbest). Readers interested in the principles of PSO are referred to [47][48][49].…”

Section: Optimization Algorithmsmentioning

confidence: 99%

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Coppel

Abellán-Nebot

Siller

et al. 2015

Int J Adv Manuf Technol

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Nowadays, the miniaturization of many consumer products is extending the use of micro-milling operations with high quality requirements. However, the impacts of cutting-tool wear on part dimensions, form and surface integrity are not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized, especially in highly tool-consuming operations like mold and die micromanufacturing. In this paper, an Adaptive Control Optimization (ACO) system is proposed to estimate cutting-tool wear in terms of part quality and adapt the cutting conditions accordingly in order to minimize the production cost, ensuring quality specifications in hardened steel micro-parts. The ACO system is based on: i) a monitoring sensor system composed of a dynamometer; ii) an estimation module with Artificial Neural Networks models; iii) an optimization module with evolutionary optimization algorithms; and iv) a CNC interface module. In order to operate in a nearly real time basis and facilitate the implementation of the ACO system, different evolutionary optimization algorithms are evaluated such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA) and Simulated Annealing (SA) in terms of accuracy, precision and robustness. The results for a given micro-milling operation showed that PSO algorithm performs better than GA and SA algorithms under computing time constraints. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3% and 29% in comparison with conservative and high production strategies, respectively.

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Particle swarm optimization based feedforward controller for a XY PZT positioning stage

Cited by 40 publications

References 49 publications

Hysteresis modeling and tracking control for a dual pneumatic artificial muscle system using Prandtl–Ishlinskii model

Hysteresis modeling and tracking control for a dual pneumatic artificial muscle system using Prandtl–Ishlinskii model

High-Precision Control of a Piezo-Driven Nanopositioner Using Fuzzy Logic Controllers

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

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