Model Based Braking Control With Support by Active Steering

Schorn, Matthias; Schmitt, J. H. M. M.; Stählin, Ulrich; Isermann, Rolf

doi:10.3182/20050703-6-cz-1902.01246

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…13 is to manipulate the steering angle sum δ w (t) such that the yaw rate behaviorψ(δ) for the driver is close to a one-track reference model. This reference model is identical with the expected steering behavior for normal driving situations with velocity-dependent behavior and includes the dynamics of lateral tire forces, [45]. The steering controller is a PID-controller with velocity-dependent parameters designed with local linear one-track models.…”

Section: Active Front Steering Control With Active Anti-roll Bar Stifmentioning

confidence: 99%

Automotive Mechatronic Systems – General Developments and Examples (Mechatronische Systeme in Kraftfahrzeugen – Allgemeine Entwicklungen und Beispiele)

Isermann¹

2006

At - Automatisierungstechnik

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Automobiles are showing an increasing integration of mechanics with digital electronics and information processing. This integration is between the components (hardware) and by the information-driven functions (software), resulting in integrated systems called mechatronic systems. Their development involves finding an optimal balance between the basic mechanical structure, sensor and actuator implementation, communication, automatic information processing and overall control. This contribution summarizes some ongoing developments for mechatronic systems in automobiles, shows design approaches and examples and considers the various embedded control functions and systems integrity. Some examples of automotive mechatronic systems are shown in more detail. Great progress can be observed in braking systems (ABS, ESP), the first brake-by-wire electro-hydraulic brake system (EHB), steering systems (electrical power steering, active front steering) and active suspension systems.Die Entwicklungen in der Fahrzeugtechnik sind durch eine zunehmende Integration von mechanischen mit digital-elektronischen und informationsverarbeitenden Komponenten (Hardware) und Datenverarbeitung (Software) geprägt. Dies führt zu integrierten Systemen, welche als mechatronische Systeme bezeichnet werden. Ihre Gestaltung schließt eine wechselseitige Optimierung von mechanischer Grundstruktur, Sensoren, Aktoren, Kommunikation und Automatisierungsfunktionen ein. Dieser Beitrag fasst einige laufende Entwicklungen mechatronischer Systeme der Kraftfahrzeug-Mechatronik zusammen, zeigt einige Entwicklungsbeispiele und betrachtet eingebettete Regelungs-und Sicherheitsfunktionen. Einige Beispiele mechatronischer Komponenten werden etwas detaillierter beschrieben. Große Fortschritte in der Serie konnten bei Bremssystemen (ABS, ESP), brake-by-wire elektrohydraulischen Bremsen (EHB), Lenksystemen (Elektrische Servolenkung, aktive Überlagerungslenkung, AFS) und semi-aktiven und aktiven Radaufhängungen erzielt werden.

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Section: Active Front Steering Control With Active Anti-roll Bar Stifmentioning

confidence: 99%

Automotive Mechatronic Systems – General Developments and Examples (Mechatronische Systeme in Kraftfahrzeugen – Allgemeine Entwicklungen und Beispiele)

Isermann¹

2006

At - Automatisierungstechnik

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“…For the realization of the chosen intervention either the active steering and/or the electro hydraulic braking system are used according to the maneuver. If a braking maneuver should be carried out, the vehicle is decelerated (Schorn et al, 2005) by utilization of the maximum force transmission available. The antilock braking system ABS supports in this case.…”

Section: Lateral Vehicle Guidancementioning

confidence: 99%

COLLISION AVOIDANCE SYSTEM PRORETA - Strategies Trajectory Control and Test Drives

Isermann

Stählin²,

Schorn³

2008

Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics Service

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Methods and experimental results of a collision avoidance driver assistance system are described with automatic object detection, trajectory prediction, and path following with controlled braking and steering. The objects are detected by a fusion of LIDAR scanning and video camera pictures resulting in the location, size and speed of objects in front of the car. A desired trajectory is calculated depending on the distance, the width of a swerving action and difference speed. For the trajectory control different control methods were designed and tested experimentally like velocity depend linear feedback and feedforward control, nonlinear asymptotic output tracking and nonlinear flatness based control using extended one-track models with vehicle state estimation for the sideslip angle and cornering stiffness. Automatic braking is realized with an electrohydraulic brake (EHB) and automatic steering with an active front steering (AFS). The various control systems are compared by simulations and real test drives showing the behaviour of a VW Golf with automatic braking or/and automatic swerving to a free track, such avoiding hitting a suddenly appearing obstacle. The research project PRORETA was a four-years-cooperation between Continental Automotive Systems and

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“…The integration of steering and braking control, namely, coordinated control, is more effective than pure steering control 5 ; thus, it has significant application in collision avoidance control. For example, model-based feedback control with simultaneous braking and steering has been introduced for collision avoidance in the studies by Schorn et al 6 and Schorn and Isermann. 7 The individual tire slip control has been developed to avoid obstacles by combining steering and braking.…”

Section: Introductionmentioning

confidence: 99%

Self-learning control for coordinated collision avoidance of automated vehicles

Wang

Yin

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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For the improvement of automotive active safety and the reduction of traffic collisions, significant efforts have been made on developing a vehicle coordinated collision avoidance system. However, the majority of the current solutions can only work in simple driving conditions, and cannot be dynamically optimized as the driving experience grows. In this study, a novel self-learning control framework for coordinated collision avoidance is proposed to address these gaps. First, a dynamic decision model is designed to provide initial braking and steering control inputs based on real-time traffic information. Then, a multilayer artificial neural networks controller is developed to optimize the braking and steering control inputs. Next, a proportional–integral–derivative feedback controller is used to track the optimized control inputs. The effectiveness of the proposed self-learning control method is evaluated using hardware-in-the-loop tests in different scenarios. Experimental results indicate that the proposed method can provide good collision avoidance control effect. Furthermore, vehicle stability during the coordinated collision avoidance control can be gradually improved by the self-learning method as the driving experience grows.

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Model Based Braking Control With Support by Active Steering

Cited by 6 publications

References 5 publications

Automotive Mechatronic Systems – General Developments and Examples (Mechatronische Systeme in Kraftfahrzeugen – Allgemeine Entwicklungen und Beispiele)

Automotive Mechatronic Systems – General Developments and Examples (Mechatronische Systeme in Kraftfahrzeugen – Allgemeine Entwicklungen und Beispiele)

COLLISION AVOIDANCE SYSTEM PRORETA - Strategies Trajectory Control and Test Drives

Self-learning control for coordinated collision avoidance of automated vehicles

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