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Dynamic neural network-based feedback linearization of electrohydraulic suspension systems

Resolving the trade-offs between suspension travel, ride comfort, road holding,
vehicle handling and power consumptions is the primary challenge in designing
Active-Vehicle-Suspension-Systems (AVSS). Controller tuning with global
optimization techniques is proposed to realise the best compromise between these
icting criteria. Optimization methods adapted include
Controlled-Random-Search (CRS), Differential-Evolution (DE), Genetic-Algorithm
(GA), Particle-Swarm-Optimization (PSO) and Pattern-Search (PS). Quarter-car
and full-car nonlinear AVSS models that incorporate electrohydraulic actuator
dynamics are designed. Two control schemes are proposed for this investigation.
The first is the conventional Proportional-Integral-Derivative (PID) control, which
is applied in a multi-loop architecture to stabilise the actuator and manipulate the
primary control variables. Global optimization-based tuning achieved enhanced
responses in each aspect of PID-based AVSS performance and a better resolve in
icting criteria, with DE performing the best. The full-car PID-based AVSS
was analysed for DE as well as modi ed variants of the PSO and CRS. These
modified methods surpassed its predecessors with a better performance index and
this was anticipated as they were augmented to permit for e cient exploration of
the search space with enhanced
exibility in the algorithms. However, DE still
maintained the best outcome in this aspect. The second method is indirect
adaptive dynamic-neural-network-based-feedback-linearization (DNNFBL), where
neural networks were trained with optimization algorithms and later feedback
linearization control was applied to it. PSO generated the most desirable results,
followed by DE. The remaining approaches exhibited signi cantly weaker results
for this control method. Such outcomes were accredited to the nature of the DE
and PSO algorithms and their superior search characteristics as well as the nature
of the problem, which now had more variables. The adaptive nature and ability to
cancel system nonlinearities saw the full-car PSO-based DNNFBL controller
outperform its PID counterpart. It achieved a better resolve between performance
criteria, minimal chatter, superior parameter sensitivity, and improved suspension
travel, roll acceleration and control force response.
Date11 September 2014
CreatorsDangor, Muhammed
Source SetsSouth African National ETD Portal
Detected LanguageEnglish
Formatapplication/pdf, application/pdf

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