Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 190

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Trajectory control of electro-hydraulic position servo system using improved PSO-PID controller

Feng, Hao, Ma, Wei, Yin, Chenbo, Cao, Donghui·2021·Automation in Construction

Abstract

Abstract This paper presents an improved particle swarm optimization (PSO) algorithm to optimize the coefficients of the proportional integral derivative (PID) controller for trajectory tracking accuracy problems. Nonlinear adaptive methods are adopted on the inertia weight to built a balance between the local and global search areas, asynchronous change strategies are introduced into the learning coefficients to rationally use individual and group experience, an elite mutation method is designed to mutate the global best position in each iteration. Basic PSO (BPSO-PID) and Ziegler-Nichols PID controller (ZN-PID) are implemented with step, sinusoidal and slope references on a co-simulation platform. The simulation results illustrate that high accuracy and fast convergence can be obtained based on the improved PSO algorithm PID controller (IPSO-PID). Experimental results demonstrated that the IPSO-PID method can achieve the highest tracking accuracy, compared with the BPSO-PID method, this method can improve the tracking accuracy by 37.14% and 50.32%, respectively.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.autcon.2021.103722

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 1 references

Research works printed but not linked: 36

  1. Palomba, 2019, Estimation of the digging and payload forces in excavators by means of state observers, Mech. Syst. Signal Process., 134, 106359, 10.1016/j.ymssp.2019.106356 · doi:10.1016/j.ymssp.2019.106356
  2. Chen, 2020, Automated excavators activity recognition and productivity analysis from construction site surveillance videos, Autom. Constr., 110, 10.1016/j.autcon.2019.103045 · DOI · Paper page
  3. Keleş, 2020, Improvement of mechanical properties by means of titanium alloying to steel teeth used in the excavator, Eng. Sci. Technol. Int. J., 23, 1208
  4. Kim, 2019, Discrete time delay control for hydraulic excavator motion control with terminal sliding mode control, Mechatronics, 60, 15, 10.1016/j.mechatronics.2019.04.008 · DOI
  5. Ranjan, 2020, A novel approach for the energy recovery and position control of a hybrid hydraulic excavator, ISA Trans., 99, 387, 10.1016/j.isatra.2019.08.066 · doi:10.1016/j.isatra.2019.08.066
  6. Zhang, 2019, The effects of control methods on energy efficiency and position tracking of an electro-hydraulic excavator equipped with zonal hydraulics, Autom. Constr., 100, 129, 10.1016/j.autcon.2019.01.003 · doi:10.1016/j.autcon.2019.01.003
  7. Morosi, 2019, Coordinated control paradigm for hydraulic excavator with haptic device, Autom. Constr., 105, 102848, 10.1016/j.autcon.2019.102848 · doi:10.1016/j.autcon.2019.102848
  8. Ha, 2000, Impedance control of a hydraulically actuated robotic excavator, Autom. Constr., 9, 421, 10.1016/S0926-5805(00)00056-X · DOI
  9. Hua, 2020, Auto-tuning nonlinear PID-type controller for rotorcraft-based aggressive transportation, Mech. Syst. Signal Process., 145, 106858, 10.1016/j.ymssp.2020.106858 · doi:10.1016/j.ymssp.2020.106858
  10. Du, 2020, Event-triggered PID control for wastewater treatment plants, J. Water Process Eng., 38, 10.1016/j.jwpe.2020.101659 · doi:10.1016/j.jwpe.2020.101659
  11. Lui, 2020, An optimal distributed PID-like control for the output containment and leader-following of heterogeneous high-order multi-agent systems, Inf. Sci., 541, 166, 10.1016/j.ins.2020.06.049 · DOI
  12. Feng, 2018, Robotic excavator trajectory control using an improved GA based PID controller, Mech. Syst. Signal Process., 105, 153, 10.1016/j.ymssp.2017.12.014 · DOI · Paper page
  13. Mousakazemi, 2019, Robust tuned PID controller with PSO based on two-point kinetic model and adaptive disturbance rejection for a PWR-type reactor, Prog. Nucl. Energy, 111, 183, 10.1016/j.pnucene.2018.11.003 · DOI
  14. Liu, 2018, Intelligent tuning method of PID parameters based on iterative learning control for atomic force microscopy, Micron, 104, 26, 10.1016/j.micron.2017.09.009 · doi:10.1016/j.micron.2017.09.009
  15. Ahmadi, 2017, Maximum power point tracking of a proton exchange membrane fuel cell system using PSO-PID controller, Int. J. Hydrog. Energy, 42, 20430, 10.1016/j.ijhydene.2017.06.208 · doi:10.1016/j.ijhydene.2017.06.208
  16. Xiang, 2019, A simple PID-based strategy for particle swarm optimization algorithm, Inf. Sci., 502, 558, 10.1016/j.ins.2019.06.042 · DOI
  17. Rout, 2019, PSO based adaptive narrowband ANC algorithm without the use of synchronization signal and secondary path estimate, Mech. Syst. Signal Process., 114, 379, 10.1016/j.ymssp.2018.05.018 · doi:10.1016/j.ymssp.2018.05.018
  18. Xu, 2013, An intelligent fault identification method of rolling bearings based on LSSVM optimized by improved PSO, Mech. Syst. Signal Process., 35, 167, 10.1016/j.ymssp.2012.09.005 · DOI
  19. Chu, 2020, Modeling and stabilization control for space-borne series-wound capturing mechanism with multi-stage damping, Mech. Syst. Signal Process., 145, 10.1016/j.ymssp.2020.106973 · doi:10.1016/j.ymssp.2020.106973
  20. Ajeil, 2020, Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm, Appl. Soft Comput., 89, 106076, 10.1016/j.asoc.2020.106076 · DOI · Paper page
  21. Wang, 2019, Control and optimization of a bionic robotic fish through a combination of CPG model and PSO, Neurocomputing, 337, 144, 10.1016/j.neucom.2019.01.062 · doi:10.1016/j.neucom.2019.01.062
  22. Li, 2019, A multi-information fusion “triple variables with iteration” inertia weight PSO algorithm and its application, Appl. Soft Comput., 84, 105677, 10.1016/j.asoc.2019.105677 · DOI
  23. Chen, 2020, Multi-objective reservoir operation using particle swarm optimization with adaptive random inertia weights, Water Sci. Eng., 13, 136, 10.1016/j.wse.2020.06.005 · DOI
  24. Jordehi, 2016, Time varying acceleration coefficients particle swarm optimisation (TVACPSO): a new optimisation algorithm for estimating parameters of PV cells and modules, Energy Convers. Manag., 129, 262, 10.1016/j.enconman.2016.09.085 · DOI
  25. Zhang, 2019, A cluster based PSO with leader updating mechanism and ring-topology for multimodal multi-objective optimization, Swarm Evolut. Comput., 50, 10.1016/j.swevo.2019.100569 · DOI
  26. Augusto, 2015, PSO with dynamic topology and random keys method applied to nuclear reactor reload, Prog. Nucl. Energy, 83, 191, 10.1016/j.pnucene.2015.03.009 · doi:10.1016/j.pnucene.2015.03.009
  27. Wang, 2014, A PSO algorithm for constrained redundancy allocation in multi-state systems with bridge topology, Comput. Ind. Eng., 68, 13, 10.1016/j.cie.2013.11.016 · doi:10.1016/j.cie.2013.11.016
  28. Pant, 2020, Prediction of clad characteristics using ANN and combined PSO-ANN algorithms in laser metal deposition process, Surf. Interfaces, 21, 100699, 10.1016/j.surfin.2020.100699 · doi:10.1016/j.surfin.2020.100699
  29. Bi, 2019, An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method, Chin. J. Chem. Eng., 27, 1888, 10.1016/j.cjche.2018.12.015 · doi:10.1016/j.cjche.2018.12.015
  30. Sharma, 2019, Sustainable automatic data clustering using hybrid PSO algorithm with mutation, Sustain. Comput. Inform. Syst., 23, 144
  31. Geoffrey, 2020, Hybrid PSO-GSA for energy efficient spectrum sensing in cognitive radio network, Phys. Commun., 40
  32. Ye, 2014, Effects of groove shape of notch on the flow characteristics of spool valve, Energy Convers. Manag., 86, 1091, 10.1016/j.enconman.2014.06.081 · DOI
  33. Feng, 2019, Identification and compensation of non-linear friction for a electro-hydraulic system, Mech. Mach. Theory, 141, 1, 10.1016/j.mechmachtheory.2019.07.004 · DOI
  34. Harrison, 2018, Optimal parameter regions and the time-dependence of control parameter values for the particle swarm optimization algorithm, Swarm Evolut. Comput., 41, 20, 10.1016/j.swevo.2018.01.006 · DOI
  35. Zhang, 2005, Optimal choice of parameters for particle swarm optimization, J. Zhejiang Univ. Sci. A, 6, 528, 10.1631/jzus.2005.A0528 · DOI
  36. Zhang, 2009, Self-organizing genetic algorithm based tuning of PID controllers, Inf. Sci., 179, 1007, 10.1016/j.ins.2008.11.038 · doi:10.1016/j.ins.2008.11.038

Metadata

Authors
Feng, H., Ma, W., Yin, C., & Cao, D.
Venue
Automation in Construction
Publisher
Elsevier BV
Published
2021-07
Volume
127
Pages
103722
ISSN
0926-5805 (ISSN-L)
OpenAlex
W3155824097
Cited by
190
References
1
FWCI
12.84
Type
journal article
Language
en
Open access
closed
Funders
  • Nanjing University of Information Science and Technology (2021R042)
  • Transformation Program of Scientific and Technological Achievements of Jiangsu Province (0714-EMTC-02-00573/4, SBA2020030413)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Trajectory control of electro-hydraulic position servo system using improved PSO-PID controller. https://app.cochat.ai/papers/10.1016/j.autcon.2021.103722
The paper
Feng, H., Ma, W., Yin, C., & Cao, D. (2021). Trajectory control of electro-hydraulic position servo system using improved PSO-PID controller. Automation in Construction. https://doi.org/10.1016/j.autcon.2021.103722

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.