Combination of map-based and adaptive feedforward control ...
Article
Combination of map-based and adaptive feedforward control algorithms for active engine mounts
Journal of Vibration and Control 1–16 ! The Author(s) 2016 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1077546315626323 jvc.sagepub.com
F Hausberg1, M Plo¨chl2, M Rupp3, P Pfeffer4 and S Hecker5
Abstract Active engine mounts significantly contribute to ensure the comfort in vehicles with emission-reducing engine technologies, e.g., cylinder-on-demand (COD), downsizing or turbochargers. To control active engine mounts, either adaptive or non-adaptive feedforward control is commonly employed. Since both approaches have previously been treated separately, this study proposes methods to connect them in terms of multiple-input-multiple-output Newton/FxLMS adaptive filters with self-trained, grid-based look-up tables. The look-up tables are incorporated as parameter-maps or parallelmaps, respectively. By combining the two feedforward control strategies, their inherent advantages, i.e., the adaptivity of adaptive filtering and the direct impact as well as the tracking behavior of map-based feedforward control, are utilized. The proposed control structures are illustrated by simulation and experimentally demonstrated in a vehicle with a V8COD engine. While both methods significantly reduce the convergence time of the adaptive filter, the parallel implementation additionally improves the tracking behavior during fast engine run-ups.
Keywords Active engine mounts, active vibration control, adaptive feedforward control
1. Introduction To meet the higher demand for low fuel consumption and legal restrictions to reduce emissions, modern engine technologies, e.g., cylinder-on-demand (COD), downsizing or turbochargers, are increasingly applied in today’s vehicles. In combination with lightweight car bodies, it becomes a more and more challenging task to satisfy the noise, vibration and harshness (NVH) expectations of the customer. Against this background, the design of engine mounts, being the main connection between the drive train and the chassis, is of particular importance. Besides supporting the static engine weight and the isolation of high-frequency (>20 Hz) engineinduced vibrations, their main task is to prevent low-frequency ( 0 for which the algorithm behaves stable and converges to the desired wo (Sayed and Rupp, 1996). Its convergence speed is defined by the eigenvalue spread of S^ H ðr!ÞSðr!Þ (Elliott, 2001). If we now replace S^ H ðr!Þ by S^ 1 ðr!Þ in equation (6), we also recognize how the above update equation changes in terms of its parameter error vector: ~ þ 1Þ ¼ wðnÞ ~ ~ wðn S^ 1 ðr!ÞSðr!ÞwðnÞ S^ 1 ðr!Þejrn!T vðnÞ
~ S^ 1 ðr!Þejrn!T vðnÞ ¼ I S^ 1 ðr!ÞSðr!Þ wðnÞ ð23Þ We have essentially the same type of algorithm, but now the eigenvalue spread of S^ 1 ðr!ÞSðr!Þ can become one and thus takes on its smallest value, resulting in fastest convergence speed. However, such speed up is obtained at the expense of potentially larger noise influence as small eigenvalues of S(r!) now enhance the noise.
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Downloaded from jvc.sagepub.com at TU Wien University Library on January 21, 2016
Combination of map-based and adaptive feedforward control algorithms for active engine mounts
Journal of Vibration and Control 1–16 ! The Author(s) 2016 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1077546315626323 jvc.sagepub.com
F Hausberg1, M Plo¨chl2, M Rupp3, P Pfeffer4 and S Hecker5
Abstract Active engine mounts significantly contribute to ensure the comfort in vehicles with emission-reducing engine technologies, e.g., cylinder-on-demand (COD), downsizing or turbochargers. To control active engine mounts, either adaptive or non-adaptive feedforward control is commonly employed. Since both approaches have previously been treated separately, this study proposes methods to connect them in terms of multiple-input-multiple-output Newton/FxLMS adaptive filters with self-trained, grid-based look-up tables. The look-up tables are incorporated as parameter-maps or parallelmaps, respectively. By combining the two feedforward control strategies, their inherent advantages, i.e., the adaptivity of adaptive filtering and the direct impact as well as the tracking behavior of map-based feedforward control, are utilized. The proposed control structures are illustrated by simulation and experimentally demonstrated in a vehicle with a V8COD engine. While both methods significantly reduce the convergence time of the adaptive filter, the parallel implementation additionally improves the tracking behavior during fast engine run-ups.
Keywords Active engine mounts, active vibration control, adaptive feedforward control
1. Introduction To meet the higher demand for low fuel consumption and legal restrictions to reduce emissions, modern engine technologies, e.g., cylinder-on-demand (COD), downsizing or turbochargers, are increasingly applied in today’s vehicles. In combination with lightweight car bodies, it becomes a more and more challenging task to satisfy the noise, vibration and harshness (NVH) expectations of the customer. Against this background, the design of engine mounts, being the main connection between the drive train and the chassis, is of particular importance. Besides supporting the static engine weight and the isolation of high-frequency (>20 Hz) engineinduced vibrations, their main task is to prevent low-frequency ( 0 for which the algorithm behaves stable and converges to the desired wo (Sayed and Rupp, 1996). Its convergence speed is defined by the eigenvalue spread of S^ H ðr!ÞSðr!Þ (Elliott, 2001). If we now replace S^ H ðr!Þ by S^ 1 ðr!Þ in equation (6), we also recognize how the above update equation changes in terms of its parameter error vector: ~ þ 1Þ ¼ wðnÞ ~ ~ wðn S^ 1 ðr!ÞSðr!ÞwðnÞ S^ 1 ðr!Þejrn!T vðnÞ
~ S^ 1 ðr!Þejrn!T vðnÞ ¼ I S^ 1 ðr!ÞSðr!Þ wðnÞ ð23Þ We have essentially the same type of algorithm, but now the eigenvalue spread of S^ 1 ðr!ÞSðr!Þ can become one and thus takes on its smallest value, resulting in fastest convergence speed. However, such speed up is obtained at the expense of potentially larger noise influence as small eigenvalues of S(r!) now enhance the noise.
ð22Þ
Downloaded from jvc.sagepub.com at TU Wien University Library on January 21, 2016
