An Offset-Free Explicit Model Predictive Pressure Controller for Integrated Braking System

Chen, Zhentao; Huang, Changyao; Liu, Zijun; Liu, Yicai; Wang, Xiangyu; Shi, Biaofei; Li, Liang

doi:10.1109/tte.2023.3298001

Cited by 2 publications

(4 citation statements)

References 35 publications

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“…( 4) with the estimated state and disturbance values using MHE in Eq. (7). The optimization problem for the lateral motion is defined as follows: 8b)).…”

Section: Motion Optimization Using Lpv-mpcmentioning

confidence: 99%

“…OF-MPC estimates and mitigates these disturbances by utilizing the estimated disturbance-free state as the actual state of the EV. Chen et al [7] applied OF-MPC to regulate the integrated brake system of AVs, accounting for model mismatches and parameter variations. Similarly, studies in [8] and [9] employed OF-MPC to address performance degradation in AV motion control due to mismatches in vehicle dynamics models.…”

Section: Introductionmentioning

confidence: 99%

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Motion Planning for Urban Autonomous Bus Considering Localization Uncertainty Using Offset-Free MPC

Jo,

Kwon,

2024

IEEE Access

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This study proposes a novel motion planning strategy to address localization uncertainty in autonomous buses. Conventional motion planning algorithms utilize information from high-definition (HD) maps to overcome the limited detection range of on-board sensors. However, this information contains uncertainty due to the utilization of estimated localization results during reference frame transformation. The wider dimensions of autonomous buses, compared to regular vehicles, amplify the potential dangers associated with localization uncertainty. Therefore, this research focuses on enhancing motion planning for autonomous buses by effectively addressing localization uncertainty. The investigation of manual driving data from autonomous buses highlights the need to handle three issues: heading bias, lateral position error, and longitudinal position error. Firstly, the heading bias was dealt with by implementing an Offset-free Model Predictive Control (OF-MPC) with a Moving Horizon Estimation (MHE) scheme for lateral motion planning. Secondly, the lateral position error was handled by incorporating a drivable corridor to determine the desired path. Lastly, the longitudinal position error was resolved by implementing a chance-constrained MPC for longitudinal motion planning. The proposed approach showed noticeable enhancement in path tracking performance while still securing ride comfort in lateral motion, collision safety, and prevention of stop-line violations. We evaluated the feasibility of the proposed approach through vehicle tests on a test track, and its applicability was further confirmed through fully autonomous driving tests on actual urban bus-only lanes.INDEX TERMS Autonomous bus, autonomous driving, chance-constrained model predictive control, localization uncertainty, moving horizon estimation, offset-free model predictive control.

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“…( 4) with the estimated state and disturbance values using MHE in Eq. (7). The optimization problem for the lateral motion is defined as follows: 8b)).…”

Section: Motion Optimization Using Lpv-mpcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Motion Planning for Urban Autonomous Bus Considering Localization Uncertainty Using Offset-Free MPC

Jo,

Kwon,

2024

IEEE Access

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“…Considering the balance between model accuracy and refinement, Zhentao Chen et al [43] used the Karnopp friction model to describe the system friction characteristics of EHB. Since experiments show that break-away force and slip friction are positively related to hydraulic pressure, hydraulic force is introduced into the Karnopp friction model.…”

Section: Experiments and Identificationmentioning

confidence: 99%

“…The system has disturbances such as model mismatch, time-varying parameters, and unexpected disturbances. Zhentao Chen et al [43] designed a disturbance observer and proposed an explicit model predictive controller. Experiments show that this control method improves hydraulic pressure control accuracy and disturbance suppression capabilities, and the real-time performance of the algorithm is verified on the microchip.…”

Section: Compensation and Controlmentioning

confidence: 99%

A Review of One-Box Electro-Hydraulic Braking System: Architecture, Control, and Application

Zhao,

Xiong,

Zhuo

et al. 2024

Sustainability

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With the development of automobile electrification and intelligence, new requirements have been put forward for automotive braking technologies. Under this background, the One-box EHB (Electro-Hydraulic Braking system) brake-by-wire technology has emerged, which combines the electric booster and wheel-cylinder control module into one box and can realize vehicle stability and comfort functions such as service brake, pedal feel simulation, brake decoupling, failure backup, active braking, and wheel-cylinder pressure control. This article reviews the current research of key technologies of One-box EHB, including system architecture design and applications under high-level autonomous driving, master cylinder pressure control algorithm design, wheel-cylinder pressure control algorithm design, and electro-hydraulic composite braking control algorithm design. Finally, this article summarizes the current research status of One-box EHB key technologies and puts forward suggestions for future research directions.

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An Offset-Free Explicit Model Predictive Pressure Controller for Integrated Braking System

Cited by 2 publications

References 35 publications

Motion Planning for Urban Autonomous Bus Considering Localization Uncertainty Using Offset-Free MPC

Motion Planning for Urban Autonomous Bus Considering Localization Uncertainty Using Offset-Free MPC

A Review of One-Box Electro-Hydraulic Braking System: Architecture, Control, and Application

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