Proportional + integral + derivative control of nonlinear full-car electrohydraulic suspensions using global and evolutionary optimization techniques

Resolving the trade-offs between suspension travel, ride comfort, road holding, vehicle handling and power consumption is the primary challenge in the design of active vehicle suspension system. Multi-loop proportional + integral + derivative controllers’ gains tuning with global and evolutionary op...

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Bibliographic Details
Published inJournal of low frequency noise, vibration, and active control Vol. 39; no. 2; pp. 393 - 415
Main Authors Dahunsi, Olurotimi A, Dangor, Muhammed, Pedro, Jimoh O, Ali, M Montaz
Format Journal Article
LanguageEnglish
Published London, England SAGE Publications 01.06.2020
Sage Publications Ltd
SAGE Publishing
Subjects
Control methods
Derivatives
Evolutionary algorithms
Evolutionary computation
Frequency analysis
Integrals
Nonlinear control
Optimization techniques
Particle swarm optimization
Passenger comfort
Power consumption
Search algorithms
Suspension systems
differential evolution
particle swarm optimization
active vehicle suspension system
Proportional + integral + derivative control
controlled random search
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Summary:Resolving the trade-offs between suspension travel, ride comfort, road holding, vehicle handling and power consumption is the primary challenge in the design of active vehicle suspension system. Multi-loop proportional + integral + derivative controllers’ gains tuning with global and evolutionary optimization techniques is proposed to realize the best compromise between these conflicting criteria for a nonlinear full-car electrohydraulic active vehicle suspension system. Global and evolutionary optimization methods adopted include: controlled random search, differential evolution, particle swarm optimization, modified particle swarm optimization and modified controlled random search. The most improved performance was achieved with the differential evolution algorithm. The modified particle swarm optimization and modified controlled random search algorithms performed better than their predecessors, with modified controlled random search performing better than modified particle swarm optimization in all aspects of performance investigated both in time and frequency domain analyses.
ISSN:1461-3484
2048-4046
DOI:10.1177/1461348419842676