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Evaluating Model Predictive Path Following and Yaw Stability Controllers for Over-Actuated Autonomous Electric Vehicles
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics.ORCID iD: 0000-0002-4504-6059
KTH, School of Engineering Sciences (SCI), Centres, VinnExcellence Center for ECO2 Vehicle design.ORCID iD: 0000-0001-8928-0368
KTH, School of Engineering Sciences (SCI), Engineering Mechanics.ORCID iD: 0000-0002-2265-9004
2020 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 69, no 11, p. 12807-12821Article in journal (Refereed) Published
Abstract [en]

Active safety systems are of significant importance for autonomous vehicles operating in safety-critical situations like an obstacle-avoidance manoeuvre with high vehicle speed or poor road condition. However, a conventional electronic stability control system, may not always yield desired path following and yaw stability performance in such circumstances merely through brake intervention. This paper pursues a detailed investigation on utilising model predictive control (MPC) and torque vectoring for path following and yaw stability control of over-actuated autonomous electric vehicles (AEVs) in dangerous double lane change manoeuvre. The control problem of the AEV is formulated based on MPC by utilising active front steering and torque vectoring, and constraints are imposed explicitly on yaw rate and sideslip angle to ensure yaw stability. Four MPC-based controllers are designed based on double-track vehicle models. Specifically, they include two one-level controllers, i.e. one with torque vectoring and one with equal torque allocation, and two two-level controllers, i.e. one with optimisation-based torque allocation and one with rule-based allocation. These controllers are assessed extensively, with respect to passing velocity, tracking accuracy, tyre utilisation and robustness. The effect of horizon length on the control performance and computational efficiency is also investigated.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2020. Vol. 69, no 11, p. 12807-12821
Keywords [en]
Tires, Wheels, Torque, Stability analysis, Resource management, Mathematical model, Electric vehicles, Model predictive control, yaw stability, torque vectoring, over-actuation, autonomous driving
National Category
Vehicle Engineering
Identifiers
URN: urn:nbn:se:kth:diva-287818DOI: 10.1109/TVT.2020.3030863ISI: 000589638700035Scopus ID: 2-s2.0-85096223558OAI: oai:DiVA.org:kth-287818DiVA, id: diva2:1522675
Note

QC 20210126

Available from: 2021-01-26 Created: 2021-01-26 Last updated: 2022-06-25Bibliographically approved
In thesis
1. Exploiting over-actuation for improved active safety of autonomous electric vehicles
Open this publication in new window or tab >>Exploiting over-actuation for improved active safety of autonomous electric vehicles
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The increasing demand for road vehicles has led to challenging road safety and environmental issues. The deployment of active safety systems and autonomous vehicles can contribute to safer roads by assisting or replacing human drivers in the task of maintaining vehicle control in critical conditions: e.g., an obstacle-avoidance manoeuvre. Road vehicle electrification can bring about environmental benefits and at the same time enable the development of over-actuated vehicle platforms. Over-actuation can be explored together with active safety and automated driving systems in order to enhance vehicle safety. On the other hand, to achieve their best possible performance, such safety and automated systems require the knowledge of vehicle states such as sideslip angle as well as reliable trajectories. However, measuring such crucial states can be overly expensive on production vehicles.

The studies presented in this thesis aim to explore how over-actuation can improve path-following and yaw-stability performance of autonomous electric vehicles in critical manoeuvres and investigate the associated state estimation and trajectory planning problems.

To achieve these goals, this thesis focuses on five aspects. First, it explores vehicle dynamics modelling by introducing vehicle and tyre models of various levels of complexity. In particular, the camber effect on lateral tyre forces was modelled using a simple, yet effective, component, which allows for individual camber control of each wheel. Second, it addresses the state estimation problem by designing and evaluating three moving horizon estimation (MHE) based estimators and an unscented Kalman filter. The evaluation in three critical manoeuvres showed that the estimator MHE outperformed the other algorithms, with improved convergence rate, accuracy and response to external disturbances and modelling errors, due to its consideration of a sequence of most recent measurements and process noises. Third, trajectory planning is studied through optimal control formulations and by examining the effect of model complexity in critical driving scenarios. It was shown that the advanced double-track planner with load transfer and the Magic Formula tyre model was desired to achieve more consistent trajectory planning and tracking performance as well as smaller peak yaw rate and sideslip angle. Fourth, the path following and yaw stability problem is tackled in the model predictive control framework and by exploring various over-actuation configurations -- active front steering (AFS), torque vectoring (TV) and active camber (AC). The results in safety-critical conditions showed that AFS + TV improved yaw stability, path following and passing velocity compared to AFS, and AFS + AC performed better than AFS + TV. The integrated control of AFS + TV + AC further enhanced vehicle safety and was more robust to reference trajectory variations, as a result of its more effective actuator and tyre utilisation. Finally, this work details the framework for optimal control implementation, which facilitates efficient computing, smooth parameter tuning and results analysis, as well as sustainable code development.

The research presented in this thesis has contributed to the modelling, formulation and control of autonomous electric vehicles by exploiting over-actuation for enhanced vehicle safety. It has been shown that over-actuation control strategies can be a promising solution for improving active safety, and thus they contribute to a safer and more sustainable future transport.

Abstract [sv]

Den ökande efterfrågan på vägfordon har orsakat utmanande trafiksäkerhets- och miljöutmaningar. Introduktionen av aktiva säkerhetssystem och autonoma fordon kan bidra till säkrare vägar genom att hjälpa eller ersätta mänskliga förare med att köra fordonet i kritiska situationer, t.ex. en undanmanöver. Dessutom kan elektrifieringen av vägfordon ge miljövinster och samtidigt möjliggöra utvecklingen av överaktuerade fordonsplattformar. Med tillgång till ökat antal aktuatorer ger det fler frihetsgrader för att styra fordonet. Detta kan utforskas tillsammans med aktiv säkerhet och autonoma fordon för att öka säkerheten. Men för att uppvisa sin bästa möjliga prestanda, kräver dessa säkerhets- och automatiserade system kunskap om fordonstillstånd och tillförlitliga fordonstrajektorier. Däremot kan kritiska fordonstillstånd som exempelvis fordonets avdriftsvinkel oftast inte mätas i produktionsfordon eftersom sensorerna är väldigt kostsamma.

Studierna som presenteras i denna avhandling syftar till att utnyttja överaktuering för att förbättra trajektorieföljning och girstabilitet hos autonoma elfordon i kritiska manövrar. Dessutom undersöks tillhörande problem med tillståndsuppskattning och trajektorieplanering.

För att nå dessa mål så handlar arbetet i denna avhandling om följande fem huvudområden. Första området handlar om fordonsdynamikmodellering och både fordons- och däckmodeller med varierande grad av komplexitet utforskas och valideras. Specifikt modelleras effekten av camber på de laterala däckkrafterna med en enkel men dock effektiv komponent som möjliggör individuell reglering av cambervinklarna för varje hjul. Det andra området belyser parameterskattning där tre MHE baserade algoritmer och en UKF baserad algoritm tas fram och utvärderas. Studien visade för tre olika kritiska körfall att standardformuleringen av MHE fungerade bättre än de andra algoritmerna och gav mer korrekta och feltoleranta skattningar med snabbare konvergering. I det tredje området så formuleras trajektorieplanering som ett optimalt reglerproblem och effekten av modellkomplexitet på trajektorieplaneringsförmågan i kritiska körfall studeras. Här visades det att planeraren med de mest avancerade modellerna presterade bättre än de andra genom högre fordonshastighet samt lägre girvinkelhastighet och avdriftsvinkel i testerna. Fjärde området handlar om trajektorieföljning och girstabilitet i ett modellprediktivt reglerramverk. Här utvärderas flera kombinationer av överaktuering, nämligen aktiv styrning (AFS), vridmomentsvektorisering (TV) och aktiv camber (AC) samt kombinationer av dem. Resultaten visar att AFS tillsammans med TV förbättrar girstabilitet, trajektorieföljning och dynamiska prestandan i testerna och att AFS i kombination med AC kunde ytterligare förbättra prestandan. Den integrerade regleringen av AFS tillsammans med TV och AC hade generellt sett den bästa prestandan, som ett resultat av mer effektivt utnyttjande av aktuatorer och däckgrepp. Slutliga området handlar om implementering av ett ramverk för regleroptimeringen som möjliggör effektiva beräkningar, underlättar parameterjusteringar och resultatanalys samt bidrar till hållbar kodutveckling.

Forskningen har bidragit till modellering, formulering och reglering av autonoma elektriska fordon genom att dra nytta av överaktuering för förbättrad fordonssäkerhet. Det har visats att reglerstrategier för överaktuering kan vara en lovande lösning för förbättrad aktiv säkerhet och därmed bidra till säkrare och miljövänligare transporter.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2022. p. 73
Series
TRITA-SCI-FOU ; 2022:15
National Category
Vehicle Engineering
Research subject
Vehicle and Maritime Engineering
Identifiers
urn:nbn:se:kth:diva-312156 (URN)978-91-8040-219-4 (ISBN)
Public defence
2022-06-13, Sal E3, Osquars backe 14, KTH, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20220513

Available from: 2022-05-13 Created: 2022-05-13 Last updated: 2022-06-25Bibliographically approved

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Zhang, WenliangDrugge, LarsNybacka, Mikael

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