Vehicle dynamics state estimation based on sensor fusion by adaptive Kalman filter

doctoral thesis

• Title (croatian): Procjena stanja dinamike vozila zasnovana na fuziji senzora primjenom adaptivnoga Kalmanova filtra
• Author: Mario Hrgetić
• Mentor: Mario Hrgetić (mentor)
• Committee member: Mario Hrgetić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; (Department of Electronic Systems and Information Processing); Zagreb
• Defense date and country: 2015, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing; Data Processing
• Universal decimal classification (UDC): 621.3 - Electrical engineering

Abstract

An increasing number of vehicle dynamics control systems are being embedded into modern vehicles in order to assure safety and comfort of driving. All of these systems require information on the vehicle dynamics state variables (e.g. yaw rate, sideslip angle, roll rate etc.). Some of them can be measured, while others need to be estimated based on available measurements and appropriate vehicle kinematics/dynamics models. This thesis presents a contribution to the research of yaw rate and sideslip angle estimation. More specifically, a kinematic sensor fusion-based yaw rate estimator has been proposed, which combines the wheel speeds measured by standard Anti-lock Braking System (ABS) sensors and the measurement of vehicle lateral acceleration obtained from two accelerometers placed diagonally upon the chassis. Similar fusion concept has been employed for development of a kinematic vehicle sideslip angle estimator utilizing information obtained by low-cost inertial sensors and single-antenna GPS receiver. Moreover, a sideslip angle estimator based on vehicle dynamics model with stochastic modeling of the tire forces has been proposed and used for concurrent estimation of other vehicle dynamics variables and parameters, such as the tire sideslip angles, lateral tire forces, tire cornering stiffness, and tire-road coefficient of friction. The research methodology includes: setup of appropriate kinematic and/or dynamic vehicle models; identification, open-loop compensation, and analysis of dominant sources of estimation errors; and design of estimators based on the sensor fusion principle by using the adaptive extended Kalman filter. Verification of the developed estimators has first been carried out by means of computer simulations based on an experimentally verified ten-degrees-of-freedom vehicle dynamics model comprising the magic-formula tire model. In the case of dynamic sideslip angle estimator with stochastic tire modeling, the estimation accuracy has also been verified experimentally, based on the data recorded on a test vehicle equipped with a high-precision inertial measurement unit and two-antenna GPS receiver, as well as by using a standard set of vehicle dynamics control system sensors. In order to obtain a favorable performance of the vehicle state variable estimation under the various operating conditions, a rule-based adaptation of the Kalman filter state covariance matrix has been utilized for kinematic estimators, while for the dynamic, model-based vehicle sideslip angle estimator an adaptive fading algorithm has been implemented for adaptation of the Kalman filter state and measurement covariance matrices.

Abstract (croatian)

U suvremena vozila ugrađuje se niz sustava aktivnog upravljanja dinamikom vozila s ciljem povećanja sigurnosti i udobnosti vožnje. Ovi sustavi zahtijevaju informacije o varijablama stanja i parametrima dinamike vozila poput brzine skretanja, kuta bočnog klizanja i kuta valjanja, inercije i mase vozila, statičkih karakteristika guma, te informacije o uvjetima na cesti (vrsti podloge tj. koeficijentu trenja kontakta guma-podloga, kutu nagiba ceste i sl.). Neke od ovih varijabli mogu se izravno mjeriti, dok je druge potrebno procijeniti na temelju dostupnih mjerenja i odgovarajućih modela kinematike ili dinamike vozila. Intenzivan razvoj raznovrsnih sustava procjene (estimatora) varijabli dinamike vozila motiviran je s jedne strane zahtjevima za smanjenjem potrebnog broja senzora, te s time povezanim smanjenjem cijene sustava upravljanja dinamikom vozila. S druge strane, u posljednje vrijeme javlja se potreba za poboljšanjem performansi konvencionalnih sustava procjene korištenjem novih senzorskih tehnologija i kombiniranjem različitih modela estimatora, odnosno primjenom postupaka sažimanja mjerenja više različitih senzora. Na taj način, uz određivanje vrijednosti veličina koje nije moguće ili nije praktično izravno mjeriti, takvi estimatori također omogućuju visoku redundanciju rekonstrukcije varijabli stanja dinamike vozila, te s time povezanu detekciju kvarova senzora i poboljšanje ukupne pouzdanosti cjelokupnog sustava upravljanja dinamikom vozila. Nadalje, sve veći broj senzora koji se ugrađuju u suvremena vozila, kao što su na primjer GPS senzori za navigaciju, inercijski senzori ili inercijske mjerne jedinice (IMU), pružaju nove mogućnosti u pogledu točnijeg i pouzdanijeg određivanja dinamičkog ponašanja vozila. Temeljem dobivenih informacija moguće je predvidjeti i spriječiti kritične situacije kao što su proklizavanje kotača, odnosno pojava podupravljanja ili preupravljanja, odnosno gubitka kontrole nad vozilom. Ovaj rad predstavlja prilog istraživanju i razvoju sustava procjene brzine skretanja i kuta bočnog klizanja vozila zasnovanih na primjeni adaptivnog Kalmanova filtra i načela fuzije (sažimanja) senzora. Pritom se razmatra i procjena popratnih parametara dinamike vozila poput gradijenta statičke karakteristike autogume za bočno gibanje i koeficijenta trenja između autogume i podloge. Metodologija istraživanja uključuje postavljanje odgovarajućih modela kinematike i dinamike vozila, analizu dominantnih izvora pogrešaka procjene dinamičkih varijabli i parametara, te sintezu i simulacijsku i eksperimentalnu provjeru razvijenih sustava procjene (estimatora).

• Keywords: state estimation; vehicle dynamics; vehicle kinematics; Kalman filter; yaw rate; sideslip angle; adaptive filtering; sensor fusion
• Keywords (croatian): procjena varijabli stanja; dinamika vozila; kinematika vozila; Kalmanov filtar; brzina skretanja vozila; kut bočnog klizanja vozila; adaptivno filtriranje; fuzija senzora
• Language: english
• URN:NBN: urn:nbn:hr:168:186386
• Study programme: Title: Electrical Engineering and Computing; Study programme type: university; Study level: postgraduate; Academic / professional title: Doktor znanosti elektrotehnike i računarstva (Doktor znanosti elektrotehnike i računarstva)
• Type of resource: Text
• Extent: 180 str. ; 30 cm.
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2019-04-16 15:47:07