Direct Identification of Optimal SM-LPV Filters and Application to Vehicle Yaw Rate Estimation

Novara, Carlo; Ruiz, Fredy; Milanese, M.

doi:10.1109/tcst.2010.2070505

Cited by 36 publications

(31 citation statements)

References 15 publications

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“…Following our data-driven philosophy, a DVS is essentially an estimator designed directly from experimental data [53]- [55]. DVS techniques have been shown to be very effective in several simulated and real-world applications [54]- [56]. 016 0.008 0.006 0.018 0.009 0.007 0.020 0.011 0.008 0.026 0.012 0.009 0.032 0.016 0.012 0.038 0. From the dataset (18), several controllers have been designed: 1) NL1: Designed following the approach of Section III-C.…”

Section: A Controller Designmentioning

confidence: 99%

Active Braking Control System Design: The D<inline-formula><tex-math>$^{\bf 2}$</tex-math> </inline-formula>-IBC Approach

Formentin

Novara

Savaresi

et al. 2015

IEEE/ASME Trans. Mechatron.

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In modern road vehicles, active braking control systems are crucial elements to ensure safety and lateral stability. Unfortunately, the wheel slip dynamics is highly nonlinear, and the online estimation of the road-tire conditions is still a challenging open-research problem. These facts make it difficult to devise a braking control system that is reliable in any possible situation without being too conservative. In this paper, we propose the data-driven inversion-based control approach to overcome the above issues. The method relies on a 2-DOF architecture, with a linear controller and a nonlinear controller in parallel, both designed using only experimental data. The effectiveness of the proposed approach against state-of-the-art braking control is shown by means of an extensive simulation campaign. A validation test on a commercial full-fledged multibody simulator for two-wheeled vehicles is also provided. Index Terms-Active braking systems, data-driven control, nonlinear control.1083-4435 di Torino. He is the author or coauthor of more than 80 scientific publications in international journals and conferences. He has been involved in several national and international projects and in several research contracts in collaboration with Italian and European companies. He is the coauthor of several patents in the automotive field. His research interests include nonlinear and LPV system identification, filtering/estimation, time-series prediction, nonlinear control, data-driven methods, set membership methods, sparse methods, and automotive, aerospace, biomedical, and sustainable energy applications.Dr. Novara is a Member of the IEEE TC on System Identification and Adaptive Control, the IFAC TC on Modeling, Identification and Signal Processing, and a Founding Member of the IEEE-CSS TC on Medical and Healthcare Systems.

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Section: A Controller Designmentioning

confidence: 99%

Active Braking Control System Design: The D<inline-formula><tex-math>$^{\bf 2}$</tex-math> </inline-formula>-IBC Approach

Formentin

Novara

Savaresi

et al. 2015

IEEE/ASME Trans. Mechatron.

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“…Even adopting the low-cost gyroscope based on microelectromechanical (MEMS) technology, it is still a relatively costly component of an ESC system. To lower the system overall cost, several attempts to estimate yaw rate have been reported in [4]- [7]. In references [4] and [5], yaw rate was estimated based on two lateral accelerometers.…”

Section: Introductionmentioning

confidence: 99%

“…In reference [6], a Kalman filterbased method was introduced which uses lateral acceleration, vehicle velocity and steering angle to estimate yaw rate. Moreover, a Set Membership (SM) method was suggested in [7] to accurately estimate yaw rate using lateral acceleration, longitudinal velocity, and steering angle measurements. However, these methods mentioned above all require an installation of lateral accelerometer, which will increase system complexity and limit their applications.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method to Estimate Yaw Rate Based on Ensemble Empirical Mode Decomposition

Chen¹,

Xu²,

Xu³

2017

dtcse

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Vehicle yaw rate is the key parameter that needs to be known by an Electronic Stability Control (ESC) system to improve the stability of the vehicle motion in emergency situations. In this paper, a novel method is proposed to estimate vehicle yaw rate which eliminates the need for the installation of conventional yaw rate sensor. In this method, yaw rate is roughly estimated by exploiting wheel speed, which is obtained from the preinstalled anti-lock braking system (ABS) wheel speed sensor. Then, a novel threshold filtering algorithm based on ensemble empirical mode decomposition (EEMD) is designed to de-noise the rough yaw rate in order to improve the accuracy of yaw rate estimation. To verify the feasibility and effectiveness of the proposed method, real road tests were performed. The results demonstrate that the proposed method has comparable performance with the gyroscope.

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“…In fact, many real systems can be approximated by LPV models which increased the interest for this framework in many different areas such as aerospace [10], automotive [11], traffic management [9], robotics [1], bio-engineering [13], gas transportation networks [7], web applications [14], etc.. Fundamental theoretic aspects on LPV modeling and identification can be seen in [15] and recent approaches to the problem are reported in [8].…”

Section: Introductionmentioning

confidence: 99%

Identification of affine linear parameter varying models for adaptive interventions in fibromyalgia treatment

Santos

Deshpande

Rivera

et al. 2013

2013 American Control Conference

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There is good evidence that naltrexone, an opioid antagonist, has a strong neuroprotective role and may be a potential drug for the treatment of fibromyalgia. In previous work, some of the authors used experimental clinical data to identify input-output linear time invariant models that were used to extract useful information about the effect of this drug on fibromyalgia symptoms. Additional factors such as anxiety, stress, mood, and headache, were considered as additive disturbances. However, it seems reasonable to think that these factors do not affect the drug actuation, but only the way in which a participant perceives how the drug actuates on herself. Under this hypothesis the linear time invariant models can be replaced by State-Space Affine Linear Parameter Varying models where the disturbances are seen as a scheduling signal signal only acting at the parameters of the output equation. In this paper a new algorithm for identifying such a model is proposed. This algorithm minimizes a quadratic criterion of the output error. Since the output error is a linear function of some parameters, the Affine Linear Parameter Varying system identification is formulated as a separable nonlinear least squares problem. Likewise other identification algorithms using gradient optimization methods several parameter derivatives are dynamical systems that must be simulated. In order to increase time efficiency a canonical parametrization that minimizes the number of systems to be simulated is chosen. The effectiveness of the algorithm is assessed in a case study where an Affine Parameter Varying Model is identified from the experimental data used in the previous study and compared with the time-invariant model.

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Direct Identification of Optimal SM-LPV Filters and Application to Vehicle Yaw Rate Estimation

Cited by 36 publications

References 15 publications

Active Braking Control System Design: The D<inline-formula><tex-math>$^{\bf 2}$</tex-math> </inline-formula>-IBC Approach

Active Braking Control System Design: The D<inline-formula><tex-math>$^{\bf 2}$</tex-math> </inline-formula>-IBC Approach

A Novel Method to Estimate Yaw Rate Based on Ensemble Empirical Mode Decomposition

Identification of affine linear parameter varying models for adaptive interventions in fibromyalgia treatment

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