The Design of an Integrated Control System in Heavy Vehicles Based on an LPV Method

Gáspár, Péter; Szabó, Zoltán; Bokor, József

doi:10.1109/cdc.2005.1583242

Cited by 28 publications

(12 citation statements)

References 19 publications

(17 reference statements)

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“…The proposed integrated model includes four Electronic ServoValve Hydraulic (ESHV) actuators (two at the front axle and two at the rear axle) in a linear single unit heavy vehicle yawroll model (Gaspar et al, 2005b). The control signal is the electrical current u opening the electronic servo-valve, the output is the force F act generated by the hydraulic actuator.…”

Section: Integrated Model For Heavy Vehiclesmentioning

confidence: 99%

Multi objective H active anti-roll bar control for heavy vehicles

et al. 2017

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In the active anti-roll bar control on heavy vehicles, roll stability and energy consumption of actuators are two essential but conflicting performance objectives. In a previous work, the authors proposed an integrated model, including four electronic servo-valve hydraulic actuators in a linear yawroll model on a single unit heavy vehicle. This paper aims to design an active anti-roll bar control and solves a Multi-Criteria Optimization (MCO) problem formulated as an H ∞ control problem where the weighting functions are optimally selected through the use of Genetic Algorithms (GAs). Thanks to GAs, the roll stability and the energy consumption are handled using a single high level parameter and illustrated via the Pareto optimality. Simulation results emphasize the simplicity and efficiency of the use of the GAs method for a MCO problem in H ∞ active anti-roll bar control on heavy vehicles.

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Section: Integrated Model For Heavy Vehiclesmentioning

confidence: 99%

Multi objective H active anti-roll bar control for heavy vehicles

et al. 2017

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“…• The parameter dependent state-space model (1) with the state vector , the input vector , the output vector , and continuous mappings , , , and . The time-dependent parameter vector depends also on a vector of measurable signals referred to as scheduling signals, according to (2) where the parameter function is a continuous mapping. The vector of scheduling signals combines external signals and internal plant signals , such that .…”

Section: Lpv Systemsmentioning

confidence: 99%

“…The comparison of and allows to assess the quality of the approximation, and-by choosing the number of scheduling variables-to trade accuracy of the model against complexity. The matrix represents a basis of the significant column space of the data matrix , and can be used to obtain a reduced mapping from to by computing (15) i.e., by applying the transformation and the scaling (12) to the mapping in (2). Note that the rows of represent the principal components of the data matrix , and that the approximate mappings , , , in (8) are related to (1) by (16) where (17) and denotes row-wise rescaling.…”

Section: B Pca Algorithm For Lpv Modelsmentioning

confidence: 99%

PCA-Based Parameter Set Mappings for LPV Models With Fewer Parameters and Less Overbounding

Kwiatkowski

Werner

2008

IEEE Trans. Contr. Syst. Technol.

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This brief presents a method for an automated generation of improved representations of linear parameter varying (LPV) systems, which is based on principal component analysis applied to typical scheduling trajectories. The procedure can help to reduce the conservatism in controller design by finding tighter regions in the space of scheduling parameters that contain the set of given trajectories. In addition, this method allows to determine approximations of LPV models with a reduced number of parameters and facilitates a systematic tradeoff between the number of parameters and the desired accuracy of the model. The proposed technique is illustrated by the application to a model of a two-link robot. Performance achieved with the controller designed using the reduced model is compared with those obtained by a robust control approach.

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“…Hence, a large number of scheduling signals results in a high computational complexity for controller synthesis. Due to this, one of the objectives in deriving an LPV model for even a highly complex system is to limit the number of scheduling variables to a very few as described in [3], [4], [5]. PCA has proven to be an effective tool for solving this problem and a handful of papers have successfully demonstrated the use of PCA for finding tighter scheduling regions for LPV systems (see [2], [7]).…”

Section: Introductionmentioning

confidence: 99%

Parameter Set-mapping using kernel-based PCA for linear parameter-varying systems

Rizvi

Mohammadpour

Tóth

et al. 2014

2014 European Control Conference (ECC)

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This paper proposes a method for reduction of scheduling dependency in linear parameter-varying (LPV) systems. In particular, both the dimension of the scheduling variable and the corresponding scheduling region are shrunk using kernel-based principal component analysis (PCA). Kernel PCA corresponds to linear PCA that is performed in a highdimensional feature space, allowing the extension of linear PCA to nonlinear dimensionality reduction. Hence, it enables the reduction of complicated coefficient dependencies which cannot be simplified in a linear subspace, giving kernel PCA an advantage over other linear techniques. This corresponds to mapping the original scheduling variables to a set of lower dimensional variables via a nonlinear mapping. However, to recover the original coefficient functions of the model, this nonlinear mapping is needed to be inverted. Such an inversion is not straightforward. The reduced scheduling variables are a nonlinear expansion of the original scheduling variables into a high-dimensional feature space, an inverse mapping for which is not available. Therefore, we cannot generally assert that such an expansion has a "pre-image" in the original scheduling region. While certain pre-image approximation algorithms are found in the literature for Gaussian kernel-based PCA, we aim to generalize the pre-image estimation algorithm to other commonly used kernels, and formulate an iterative pre-image estimation rule. Finally, we consider the case study of a physical system described by an LPV model and compare the performance of linear and kernel PCA-based LPV model reduction.

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The Design of an Integrated Control System in Heavy Vehicles Based on an LPV Method

Cited by 28 publications

References 19 publications

Multi objective H active anti-roll bar control for heavy vehicles

Multi objective H active anti-roll bar control for heavy vehicles

PCA-Based Parameter Set Mappings for LPV Models With Fewer Parameters and Less Overbounding

Parameter Set-mapping using kernel-based PCA for linear parameter-varying systems

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