A Minimum Zone Method for Evaluating Straightness Errors Using PSO Algorithm

Zhang, Ke

doi:10.1007/978-3-540-87442-3_39

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…The study has found that PSO has better performance in terms of solution consistencies and convergence than the other swarm algorithms. Zhang [21] has also stated that PSO has better convergence in achieving global optimum with better accuracy than conventional methods. Also, with a lesser number of algorithm parameters to tweak than genetic algorithm (GA), PSO is always preferable.…”

Section: Building Knowledge Database Using Particle Swarm Optimisationmentioning

confidence: 99%

Knowledge-Based Controller Optimised with Particle Swarm Optimisation for Adaptive Path Tracking Control of an Autonomous Heavy Vehicle

Amer¹,

Hudha²,

Zamzuri³

et al. 2021

Automation and Control

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This chapter discusses the development of an adaptive path tracking controller equipped with a knowledge-based supervisory algorithm for an autonomous heavy vehicle. The controller was developed based on a geometric/kinematic controller, the Stanley controller. One of the mostly known issues with any geometric/kinematic controller is that a properly tuned controller may not be valid in a different operating region than the one it was being tuned/optimised on. Therefore, this study proposes an adaptive algorithm to automatically choose an optimal controller parameter depending on the manoeuvring and vehicle conditions. An optimal knowledge database is developed for an adaptive algorithm to automatically obtain the parameter values based on the vehicle speed, v, and heading error, ϕ. Several simulations are carried out with different trajectories and speeds to evaluate the effectiveness of the controller against its predecessors, namely, Stanley and the nonadaptive modified Stanley (Mod St) controllers. The simulated steering actions are then compared against human driver's experimental data along the predefined paths. It was shown that the proposed adaptive algorithm managed to guide the heavy vehicle successfully and adapt to various trajectories with different vehicle speeds while recording lateral error improvement of up to 82% compared to the original Stanley controller.

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Section: Building Knowledge Database Using Particle Swarm Optimisationmentioning

confidence: 99%

Knowledge-Based Controller Optimised with Particle Swarm Optimisation for Adaptive Path Tracking Control of an Autonomous Heavy Vehicle

Amer¹,

Hudha²,

Zamzuri³

et al. 2021

Automation and Control

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“…Content may change prior to final publication. algorithm [36], and the results are in Tables 1 to 4.…”

Section: Mean Euler-poincaré Characteristic and Decomposed Mean Euler-poincaré Characteristicmentioning

confidence: 99%

Decomposed Mean Euler-Poincaré Characteristic Model for a Non-Gaussian Physiological Random Field

Ramos-Martinez

Corbier²,

Alvarado

et al. 2021

IEEE Access

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This paper introduces a new approach of the mean Euler-Poincaré characteristic for non-Gaussian random fields (NGRF), which is based on the decomposition by a basic function named motherwave. The method is proved for long-term recorded, noisy physiological signals. A pretreatment allows the signal to become smooth as the original one is fitted through a Random Algebraic Polynomials (RAP)-based scheme. After that, the polynomized signals are merged by thresholding the RAP function at different levels u. In this way, it is formed a real-valued non-Gaussian physiological random field (NGPRF). Thereby, we deal with their geometric properties centered on their excursion sets A u (Φ, T ) and a topological invariant, such as the Euler Poincaré Characteristic (EPC) ϕ(A u (Φ, T )). The highlight of this work is an explicit model, referred to as the decomposed mean Euler-Poincaré characteristic (DMEPC). The proposed method produces a reduced model with a viable interpretation for different heart conditions investigated for data issued from Holter EKG recordings.

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“…The objective function for the tolerance zone considering the distance between the two parallel lines can be given as 17 …”

Section: Accuracy Analysis Of the Msspmentioning

confidence: 99%

A novel control strategy for the multi-step straightening process of long/extra-long linear guideways

Zhang

Lü

Zhang

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part C:

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The straightening process for a linear guideway with particular cross-section shape is normally conducted by the three-point pressure bending method. However, the single-step straightening process (SSSP) of a long/extra-long linear guideway may make the workpiece from a single-curvature curve into a more complex shape. Due to these limitations of SSSP, a quantitative control strategy for the multi-step straightening process (MSSP) of a long/extra-long linear guideway is proposed in this paper based on the straightening principle of SSSP. Firstly, the predictive models for straightening stroke and helix angle after unloading with respect to SSSP are developed based on the elasto-plastic theory and curvature integral model. Depending on the established analytical model for SSSP, the MSSP is then mathematically modelled to obtain corresponding straightening parameters considering feeding process, clamping process and straightening process. Besides, the finite element method has been applied to validate the developed mathematical model for the MSSP. Taking the approach of a linear guideway as an example, the experimental results have also shown that the proposed control strategy is appropriate for the MSSP of a long/extra-long linear guideway.

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A Minimum Zone Method for Evaluating Straightness Errors Using PSO Algorithm

Cited by 5 publications

References 12 publications

Knowledge-Based Controller Optimised with Particle Swarm Optimisation for Adaptive Path Tracking Control of an Autonomous Heavy Vehicle

Knowledge-Based Controller Optimised with Particle Swarm Optimisation for Adaptive Path Tracking Control of an Autonomous Heavy Vehicle

Decomposed Mean Euler-Poincaré Characteristic Model for a Non-Gaussian Physiological Random Field

A novel control strategy for the multi-step straightening process of long/extra-long linear guideways

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