Robust Vehicle Roll Dynamics Identification based on Roll Rate Measurements

Tafner, Robert; Reichhartinger, Markus; Horn, Martin

doi:10.3182/20121023-3-fr-4025.00047

Cited by 6 publications

(7 citation statements)

References 17 publications

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“…Therefore, in this paper, a DTKF is applied to simultaneously estimate the roll angle, the state variable, and the lateral acceleration, the disturbance, using the measured roll rate signal. For use in observers, the state variables and state vector are defined as (29) [19,28]. The continuous-time state-space equation according to the vector of state variables is derived as (30) from (1) and (4).…”

Section: Disturbance Observer Designmentioning

confidence: 99%

“…So, it is necessary to estimate parameters of a vehicle model for better state estimation. For the purpose, uncertain or time-varying parameters have been estimated by parameter estimators such as least-meansquare (LMS) and recursive-least-square (RLS) algorithms [28][29][30][31]. For ARS, RLS was adopted to estimate the height of center of gravity [28].…”

Section: Introductionmentioning

confidence: 99%

“…For ARS, RLS was adopted to estimate the height of center of gravity [28]. Roll dynamics was identified with RLS for sliding mode state observer [29]. It has been known that an extended Kalman filter (EKF) is equivalent to RLS.…”

Section: Introductionmentioning

confidence: 99%

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Active Roll Stabilization With Disturbance Feedforward Control

Yim

2021

IEEE Access

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This paper presents a method to design a controller for active roll stabilization (ARS) with a disturbance feedforward control. To design the controller, a linear 1-DOF roll model is adopted. With the model, three feedback controllers, i.e., linear quadratic regulator (LQR), H and sliding mode controller (SMC), are designed. Feedforward controller with the lateral acceleration is designed with a discrete-time state-space equation to improve roll control performance. To estimate the roll angle and the lateral acceleration simultaneously with noisy roll rate measurement, a discrete-time Kalman filter (DTKF) is used. When applying DTKF for state and disturbance estimation, a recursive least square (RLS) is adopted to estimate the parameters of 1-DOF roll model. As an actuator for ARS, an active anti-roll bar (AARB) and a continuous damping control (CDC) are adopted. To verify the performance of the proposed controllers, simulation is conducted on the vehicle simulation package, CarSim . From simulation, it was verified that the proposed controllers can enhance the performance of active roll stabilization and that the effectiveness of CDC was investigated. INDEX TERMS active roll stabilization (ARS), disturbance feedforward control, discrete-time Kalman filter (DTKF), recursive least square (RLS), active anti-roll bar (AARB), continuous damping control (CDC)

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Section: Disturbance Observer Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Active Roll Stabilization With Disturbance Feedforward Control

Yim

2021

IEEE Access

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“…For roll angle estimation, RLS was used to estimate the height of center of gravity [20]. Roll dynamics was identified with RLS for sliding mode state observer [21]. Compared to robust estimation schemes, this is simple to design and easy to implement.…”

Section: Introductionmentioning

confidence: 99%

Design of Robust Observers for Active Roll Control

Park

Yim

2019

IEEE Access

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This paper presents robust observers for active roll control. Generally, active roll control adopts feedback control structure with roll angle and roll rate signals which should be measured with a sensor. For active roll control, it is necessary to estimate the roll angle with roll rate measurement using an observer because the roll angle is difficult to measure, compared to the roll rate. However, an observer designed with fixed parameters can give poor estimation performance since there are large uncertainty in parameters of real vehicles. A robust observer is designed to cope with the parameter uncertainty. In this paper, a dual Kalman filter and Kalman filter with constant velocity model are adopted as a robust observer. The performance of the proposed algorithm has been investigated via computer simulations and vehicle tests.

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“…With the rapid development of modern industrial technology, the research on the dynamic characteristics, fatigue durability, and robustness of the vehicle takes an important place in the development of the vehicle industry. 1,2 The analysis of dynamic characteristics as a key link in the design process provides significant guiding purpose for the rationality of structural design. In addition, the research on durability and robustness has important value on shortening the development cycle, reducing research and development costs, preventing structural failure, and re-optimizing the structure.…”

Section: Introductionmentioning

confidence: 99%

Research on dynamic characteristics and fatigue robust optimization of integrated vehicle model

Yuan

Zhao

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper investigates the dynamic characteristics and fatigue robust optimization of heavy-duty tractor. First, this paper presents a vehicle model with sub-structure method. Based on the theory of base motion, the structure dynamic characteristics are analyzed. Second, the accuracy of the method is verified by comparing the experimental results with the simulation results. Also, the dynamic response and the transfer function of vehicle are obtained using the above methods. Combined with the experimental data, the methods of random multiple frequency components and multi-axial fatigue life are adopted to analyze the fatigue damage of the heavy-duty tractor under different road conditions. Finally, the Design for Six Sigma is used to optimize the vehicle’s structure. The results show that by using the proposed method, the dynamic characteristics of the vehicle can be analyzed accurately and effectively, robustness of the vehicle can be improved, and mass of the vehicle can be reduced.

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Robust Vehicle Roll Dynamics Identification based on Roll Rate Measurements

Cited by 6 publications

References 17 publications

Active Roll Stabilization With Disturbance Feedforward Control

Active Roll Stabilization With Disturbance Feedforward Control

Design of Robust Observers for Active Roll Control

Research on dynamic characteristics and fatigue robust optimization of integrated vehicle model

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