Development of Mando's New Continuously Controlled Semi-Active Suspension System

Kim, Wanil; Lee, Jeongwoo; Yoon, Sangho; Kim, Dongshin

doi:10.4271/2005-01-1721

Cited by 10 publications

(8 citation statements)

References 3 publications

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“…A number of studies have examined the potential of driving modes in areas such as fuel/energy management [10][11][12], chassis control [13][14][15], and adaptation to personal driving styles [16]. The existing literature focuses on the functionality of individual active vehicle components, where a distinction can be made between active components that affect longitudinal/lateral (Section 1.2) and vertical vehicle dynamics (Section 1.3).…”

Section: Driving Modesmentioning

confidence: 99%

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How do driving modes affect the vehicle’s dynamic behaviour? Comparing Renault’s Multi-Sense sport and comfort modes during on-road naturalistic driving

Melman

Winter

Mouton

et al. 2019

Vehicle System Dynamics

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Several modern vehicles provide the option to select a driving mode. However, the literature contains no empirical studies that investigate how driving modes affect the vehicle's dynamic behaviour in regular on-road driving. We examined for which CAN-bus signals the differences between Renault's Multi-Sense R comfort and sport modes are most apparent. We gathered data on a 26.3 km route containing a rural and highway section. A single person drove the route four times in comfort mode and four times in sport mode. By statistically analysing and ordering 887 CAN-bus signals, we found strong differences between the two modes for rear-wheel angle, engine torque, longitudinal acceleration, and vertical motion. Parameter identification of a quarter car model identified a 3.5 times higher damping coefficient for the sport mode compared to the comfort mode. Due to four wheel steering, compared to the comfort mode, the sport mode yielded a higher lateral acceleration and yaw rate for a given steering wheel angle and driving speed. In conclusion, this study provides quantitative insight into the extent to which the Multi-Sense driving modes impact the vehicle's lateral, longitudinal, and vertical dynamic behaviour. The results and the analysis methods help guide future driving mode designs.

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Section: Driving Modesmentioning

confidence: 99%

“…Driving modes could affect the functionality of these components [13,14]. For example, for a sport driving mode, a higher variable damping parameter could be utilised to feed more vibrations to the driver.…”

Section: Active Vertical Vehicle Dynamicsmentioning

confidence: 99%

How do driving modes affect the vehicle’s dynamic behaviour? Comparing Renault’s Multi-Sense sport and comfort modes during on-road naturalistic driving

Melman

Winter

Mouton

et al. 2019

Vehicle System Dynamics

View full text Add to dashboard Cite

show abstract

“…In particular, Chamseddine [19] used a sliding mode observer based on a quarter car model to detect sensor failures in vehicle systems. Although fault detection algorithms are robust to interferences, additional sensors such as displacement sensors are still used to replace the commonly used sensor configurations in the commercial vehicles [20]. In References [21,22], the parity check space method, which is typical model-based method, is proposed for fault detection.…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle

Geng

Чулин

Wang

2020

Sensors

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The fault detection and isolation are very important for the driving safety of autonomous vehicles. At present, scholars have conducted extensive research on model-based fault detection and isolation algorithms in vehicle systems, but few of them have been applied for path tracking control. This paper determines the conditions for model establishment of a single-track 3-DOF vehicle dynamics model and then performs Taylor expansion for modeling linearization. On the basis of that, a novel fault-tolerant model predictive control algorithm (FTMPC) is proposed for robust path tracking control of autonomous vehicle. First, the linear time-varying model predictive control algorithm for lateral motion control of vehicle is designed by constructing the objective function and considering the front wheel declination and dynamic constraint of tire cornering. Then, the motion state information obtained by multi-sensory perception systems of vision, GPS, and LIDAR is fused by using an improved weighted fusion algorithm based on the output error variance. A novel fault signal detection algorithm based on Kalman filtering and Chi-square detector is also designed in our work. The output of the fault signal detector is a fault detection matrix. Finally, the fault signals are isolated by multiplication of signal matrix, fault detection matrix, and weight matrix in the process of data fusion. The effectiveness of the proposed method is validated with simulation experiment of lane changing path tracking control. The comparative analysis of simulation results shows that the proposed method can achieve the expected fault-tolerant performance and much better path tracking control performance in case of sensor failure.

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“…The continuous damping control (CDC) system is a type of semi-active suspension system that continuously controls the damping coefficients by altering the area of the hole through which oil pressure is applied [19]. Several different control methods are used to control the CDC system, such as sky-hook control [20,21], adaptive control [22,23], optimal control [24], robust control [25], sliding mode control [26], fuzzy control [27], preview control [28], and model predictive control [29].…”

Section: Introductionmentioning

confidence: 99%

Sensor fault detection and isolation algorithm for a continuous damping control system

Kim

Lee

2011

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper presents an analytical sensor fault-detection and isolation algorithm for the vertical accelerometers of a continuous damping control (CDC) system, which are essential but vulnerable components in the CDC system. Because the sensor configuration of the target CDC system does not provide sufficient redundancy for fault diagnosis, two different vehicle suspension models, a modified full car model and a roll-and-pitch plane model, are derived and combined to establish analytical redundancy. Parity equations for the vertical motion of a sprung mass are derived using the combined model and applied in order to detect faults of the vertical accelerometers. In order to isolate the faults properly in practical situations, this paper proposes a fault isolation algorithm which calculates estimates of the vertical acceleration at the centre of gravity of the sprung mass using vertical acceleration signals and compares the residuals of these estimates. This paper also provides a detailed analysis regarding the detectability and isolability of vertical accelerometer faults. An adaptive threshold method is proposed to improve the performance of the suggested fault-detection and isolation algorithm. Simulation and test results demonstrate the feasibility and effectiveness of the proposed algorithm.

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Development of Mando's New Continuously Controlled Semi-Active Suspension System

Cited by 10 publications

References 3 publications

How do driving modes affect the vehicle’s dynamic behaviour? Comparing Renault’s Multi-Sense sport and comfort modes during on-road naturalistic driving

How do driving modes affect the vehicle’s dynamic behaviour? Comparing Renault’s Multi-Sense sport and comfort modes during on-road naturalistic driving

Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle

Sensor fault detection and isolation algorithm for a continuous damping control system

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