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Record W2793050506 · doi:10.14313/jamris_4-2017/34

Design and Development of a Semi-active Suspension System for a Quarter Car model using PI Controller

2018· article· en· W2793050506 on OpenAlexaboutno aff
Hudyjawa Siswoyo, Nazim Mir‐Nasiri, Hazrat Ali

Bibliographic record

VenueJournal of Automation Mobile Robotics & Intelligent Systems · 2018
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Car modelSuspension (topology)PID controllerController (irrigation)Active suspensionControl theory (sociology)EngineeringComputer scienceAutomotive engineeringControl engineeringMathematicsControl (management)Artificial intelligenceGeographyBiologyTemperature control

Abstract

fetched live from OpenAlex

This paper presents the design and development of a semi-active suspension system for a vehicle. The main idea is to develop a system that is able to damp vibration of the vehicle body while crossing the bumps on the road. This system is modeled for a single wheel assembly and then the laboratory prototype of the complete system has been manufactured. It is used to physically simulate the spring-mass-damper system in vehicle and observe the frequency response to the external disturbances. The developed low-cost smart experimental equipment consists of a motor with offset mass which works as an oscillator to induce vibration, a spring-mass-damper system where the variable damper works as a pneumatic cylinder that allows varying the damping constant (c). Proportional-Integral (PI) controller is used to control the damping properties of the semi-active suspension system automatically. The system is designed in contrast to the most of the available suspension systems in the market that have only passive damping properties. The results of this research demonstrate the efficiency of the developed variable damper-based control system for the vehicle suspension system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.884
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.260
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Automation Mobile Robotics & Intelligent SystemsSame topicVibration Control and Rheological FluidsFrench-language works237,207