LPV H&lt;inf&gt;2&lt;/inf&gt; control of semi-active suspensions

Esmaeili, Jaffar Seyyed; Akbari, Ahmad; Mirzaei, Mehdi

doi:10.1109/icciautom.2011.6356749

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…Theorem 1. The closed-loop system will be stable, and the H 2 norm from exogenous inputs to controlled outputs will be less than γ if, and only if, there exist symmetric positive definite matrices, X and Z, that satisfy the following inequalities [33]:…”

Section: 𝑦(𝑡) = 𝐶 𝑥(𝑡) + 𝐷 𝑤(𝑡) + 𝐷 𝑢(𝑡)mentioning

confidence: 99%

“…Consequently, the H 2 norm is frequently regarded as the preferred option, as it is typically less conservative than the H ∞ norm when quantifying ride comfort in scenarios where the system is subjected to white noise or impulse inputs. As a result, many researchers have explored the use of Linear Quadratic Gaussian (LQG) and H 2 norm minimization in the design of active vehicle suspension systems [2,[31][32][33].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Neuro-Fuzzy Control of Active Vehicle Suspension Based on H2 and H∞ Synthesis

Esmaeili,

Akbari,

Farnam

et al. 2023

Machines

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This paper addresses the issue of a road-type-adaptive control strategy aimed at enhancing suspension performance. H2 synthesis is employed for modeling road irregularities as impulses or white noise, minimizing the root mean square (RMS) of performance outputs for these specific road types. It should be noted, however, that this approach may lead to suboptimal performance when applied to other road profiles. In contrast, the H∞ controller is employed to minimize the RMS of performance outputs under worst-case road irregularities, taking a conservative stance that ensures robustness across all road profiles. To leverage the advantages of both controllers and achieve overall improved suspension performance, automatic switching between these controllers is recommended based on the identified road type. To implement this adaptive switching mechanism, manual switching is performed, gathering input–output data from the controllers. These data are subsequently employed for training an Adaptive Neuro-Fuzzy Inference System (ANFIS) network. This elegant approach contributes significantly to the optimization of suspension performance. The simulation results employing this novel ANFIS-based controller demonstrate substantial performance enhancements compared to both the H2 and H∞ controllers. Notably, the ANFIS-based controller exhibits a remarkable 62% improvement in vehicle body comfort and a significant 57% enhancement in ride safety compared to passive suspension, highlighting its potential for superior suspension performance across diverse road conditions.

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Section: 𝑦(𝑡) = 𝐶 𝑥(𝑡) + 𝐷 𝑤(𝑡) + 𝐷 𝑢(𝑡)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Control of Active Vehicle Suspension Based on H2 and H∞ Synthesis

Esmaeili,

Akbari,

Farnam

et al. 2023

Machines

View full text Add to dashboard Cite

show abstract

Review on LPV Approaches for Suspension Systems

Sename

2021

Electronics

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This paper presents a detailed literature review about Linear Parameter Varying (LPV) approaches applied to vehicle suspension systems. Indeed many works have been devoted to vehicle (active and semi-active) suspension in the past 20 years, because this subsystem in the only one affecting passenger comfort and road holding. Moreover several studies have also been concerned with global vehicle dynamic control using the suspension systems in collaboration with other subsystems (steering, braking …). On the other hand, the LPV approaches have proved to be very efficient to control non linear systems as well as to provide some kind of adaptive control. Naturally many LPV methods have been developed for suspension systems in order to take into account the nonlinear characteristics of the dampers, to adapt the suspension performance to the passenger request or to the road profile, to make the suspension systems collaborate with other subsystems, or to provide a fault tolerant control in case of damper loss of efficiency. This survey paper will make a deep analysis about the recent studies dedicated to vehicle suspension systems aiming at providing a better insight on the type of LPV methods that have been considered.

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LPV H<inf>2</inf> control of semi-active suspensions

Cited by 2 publications

References 10 publications

Adaptive Neuro-Fuzzy Control of Active Vehicle Suspension Based on H2 and H∞ Synthesis

Adaptive Neuro-Fuzzy Control of Active Vehicle Suspension Based on H2 and H∞ Synthesis

Review on LPV Approaches for Suspension Systems

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