Vehicle Roll Dynamics Research Articles

Analysis and Control of the Vehicle Roll Dynamics Using Sum of Squares Polynomial Approach

Analysis and Control of the Vehicle Roll Dynamics Using Sum of Squares Polynomial Approach

Tire normal force estimation using artificial neural networks and fuzzy classifiers: Experimental validation

Tire normal forces play a crucial role in vehicle dynamic control systems (VDCs), and an accurate estimation of them could substantially improve vehicle handling and safety. Total normal forces on tires are the combination of the static and dynamic values. These values change by varying the vehicle static parameters (vehicle mass and CG position), the road grade, and vehicle dynamic states. All of the mentioned forces and parameters are used in the design of vehicle dynamics controllers. Therefore, to have an efficient estimation solution to access all different target controllers, the proposed estimation algorithm is divided into two parts. The first part is an ANN-based vehicle mass and CG position estimation algorithm and the second one is a deep-learning-based algorithm and an integrated hardware–software method to estimate dynamic normal forces. The first part includes two neural network (NN) blocks related to vehicle roll and pitch dynamics. The outputs of the NN blocks are load distributions for the axes associated with each dynamics. Based on the measurement unit data, the vehicle’s maneuvers are classified into one of many categories used for downstream tasks. The rules of the fuzzy classification logic determine which of the mentioned blocks to be activated. At the same time, an experimentally validated 9-DOF vehicle model instantaneously observes the estimated values. In the second part, long short-term memory (LSTM), gated recurrent unit (GRU), and an integrated hardware–software method were developed to assess the tire’s dynamic normal forces during maneuvers. The performed simulations and the results of field tests show that the proposed algorithm can accurately estimate the considered parameters in the presence of noise and disturbances. It is shown that the proposed algorithm is more accurate and faster than the extended Kalman filter and recursive least square estimation methods to estimate static values.

Trajectory Planning of Autonomous Trucks for Collision Avoidance With Rollover Prevention

Autonomous trucks are considered as one of the most promising developments for future commercial vehicles. However, the impact of roll dynamics, which is well known as a crucial safety factor for conventional human-driven trucks, is not well studied for the trajectory planning of autonomous trucks. In this study, a trajectory planning strategy of autonomous trucks is proposed for collision avoidance scenarios with rollover prevention. The proposed three-step planning strategy can ensure both vehicle lateral and roll stability and safety, and simultaneously preserve the optimality of the planning solutions in terms of the total traveling time and distance for the whole maneuver. Specifically, the impact of vehicle roll dynamics on the trajectory planning of autonomous trucks is studied. The effectiveness and feasibility of the proposed trajectory planning strategy are evaluated via co-simulation between Matlab/Simulink and TruckSim.

Artificial Intelligence for Stability Control of Actuated In–Wheel Electric Vehicles with CarSim® Validation

This paper presents an active controller for electric vehicles in which active front steering and torque vectoring are control actions combined to improve the vehicle driving safety. The electric powertrain consists of four independent in–wheel electric motors situated on each corner. The control approach relies on an inverse optimal controller based on a neural network identifier of the vehicle plant. Moreover, to minimize the number of sensors needed for control purposes, the authors present a discrete–time reduced–order state observer for the estimation of vehicle lateral and roll dynamics. The use of a neural network identifier presents some interesting advantages. Notably, unlike standard strategies, the proposed approach avoids the use of tire lateral forces or Pacejka’s tire parameters. In fact, the neural identification provides an input–affine model in which these quantities are absorbed by neural synaptic weights adapted online by an extended Kalman filter. From a practical standpoint, this eliminates the need of additional sensors, model tuning, or estimation stages. In addition, the yaw angle command given by the controller is converted into electric motor torques in order to ensure safe driving conditions. The mathematical models used to describe the electric machines are able to reproduce the dynamic behavior of Elaphe M700 in–wheel electric motors. Finally, quality and performances of the proposed control strategy are discussed in simulation, using a CarSim® full vehicle model running through a double–lane change maneuver.

Rollover Index for Rollover Mitigation Function of Intelligent Commercial Vehicle’s Electronic Stability Control

This paper describes a rollover index for detection or prediction of impending rollover in different driving situations using minimum sensor signals which can be easily obtained from an electronic stability control (ESC) system. The estimated lateral load transfer ratio (LTR) was used as a rollover index with only limited information such as the roll state of the vehicle and some constant parameters. A commercial vehicle has parameter uncertainties because of its load variation. This is likely to affect the driving performance and the estimation of the dynamic state of the vehicle. The main purpose of this paper is to determine the rollover index based on reliable measurements and the parameters of the vehicle. For this purpose, a simplified lateral and vertical vehicle dynamic model was used with some assumptions. The index is appropriate for various situations although the vehicle parameters may change. As part of the index, the road bank angle was investigated in this study, using limited information. Since the vehicle roll dynamics are affected by the road bank angle, the road bank angle should be incorporated, although previous studies ignore this factor in order to simplify the problem. Because it increases or reduces the chances of rollover, consideration of the road bank angle is indispensable in the rollover detection and mitigation function of the ESC system. The performance of the proposed algorithm was investigated via computer simulation studies. The simulation studies showed that the proposed estimation method of the LTR and road bank angle with limited sensor information followed the actual LTR value, reducing the parameter uncertainties. The simulation model was constructed based on a heavy bus (12 tons).

Prediction of the subjective impression of passenger car roll dynamics on the driver based on frequency-domain characteristic values

Characteristic values are essential for the design and assessment of driving dynamics during the early stages of the development process of passenger cars. Compared to other aspects of vehicle dynamics however, the relationship between measurable parameters and the subjective perception of vehicle roll dynamics has not been researched extensively. In this paper, a study is presented in which several variants of a vehicle with an electronically controlled suspension were rated by test subjects regarding its roll dynamics and measured in a standardised driving manoeuvre. The subjective ratings and objective characteristic values were then used to derive models to predict the subjective liking of several roll dynamics aspects based on objective frequency-domain parameters which could be achieved with relatively high accuracy. The resulting prediction models were validated using measurements of additional vehicles even though this only allowed for a rough validation as no subjective ratings were available for the validation set. Nevertheless, the predictions for the validation variants seemed plausible and reflected the basic expectations regarding the vehicle characters of the validation set. The results of this investigation thus laid a good foundation for follow-up studies to further increase the reliability and robustness of the prediction models.

Active Roll Control System Design With Considering Actuator Constraints for Passenger Car

Abstract This paper proposes an active roll control system for passenger cars. The roll actuator with electric motor is expected to replace the hydraulic roll control actuator with the better performance. In order to meet those expectations, highly precise control methods are needed for adjusting the roll motion of the vehicle. In this study, roll dynamics of vehicle and the properties of active roll actuators are investigated first including the latency of the actuator. The identification method is designed to estimate the key parameters of the suspension system. The reference model method is proposed to determine the target roll states and the model predictive control (MPC) method is adopted to control the rolling motion. The roll moment distribution method is also designed between the front and rear actuators considering the load transfer. The proposed control system is validated via simulations and experiments.

Fuzzy Observer-Based Prescribed Performance Control of Vehicle Roll Behavior via Controllable Damper

This paper presents a novel observer-based control strategy to improve the vehicle roll behavior performance through magneto-rheological (MR) dampers under steering wheel input and various road excitation conditions. Since the vehicle roll with sudden steering input is an essential part of driving safety and possesses inherent nonlinearities, the full-car nonlinear Takagi-Sugeno (T-S) fuzzy model is first established to describe the vehicle roll dynamics considering nonlinear coupling dynamics of tire lateral force and MR damper force under road excitation input. Furthermore, a T-S model-based fuzzy observer is adapted to estimate the vehicle roll angle and roll rate. The stability conditions for the used T-S observer are calculated using linear matrix inequalities (LMIs), and the proposed observer is induced by solving the proposed LMI. Based on the Lyapunov function, sliding mode theory and prescribed performance function, a novel state observer-based prescribed performance control strategy is developed to constrain the controlled vehicle roll angle and roll rate state within the prescribed performance boundaries. Finally, the proposed techniques are validated through the J-turn and Fishhook tests conducted via a high-fidelity CarSim software platform.

Model predictive control of vehicle roll-over with experimental verification

This paper presents a model predictive approach to directly control untripped vehicle roll-over. First, a novel real-time estimation scheme is designed to provide the vehicle roll angle using an observer on a combined model of vehicle kinematics and roll dynamics. This angle is used in an integrated directional and roll dynamics model for the prediction of vehicle states and roll-over index. The roll-over prevention objective is specified as a soft constraint to ensure persistent feasibility. If the controller foresees impending vehicle roll-over, it intervenes and reduces the roll-over index by torque vectoring. Software simulations are used to assess the effectiveness of the proposed roll-over control method in the industry standard fishhook maneuvers. In addition, the accuracy and performance of the suggested estimation and control algorithms are verified in double-lane change and flick maneuvers on dry asphalt with an instrumented test vehicle. The results show accurate estimation of the vehicle roll angle and excellent control on roll-over index with the proposed predictive roll-over controller.

Design and experimental validation of control algorithm for vehicle hydraulic active stabilizer bar system

This paper presents a novel active roll control algorithm for vehicle hydraulic active stabilizer bar system. The mechanical structure and control scheme of hydraulic active stabilizer bar system is detailed. The anti-roll torque controller is designed with “Proportional-Integral-Differential (PID) + feedforward” algorithm to calculate the total anti-roll torque. A lateral acceleration gain and roll rate damping are added into “PID + feedforward” controller, which can improve vehicle roll dynamic response. The torque distributor is introduced based on fuzzy–PID algorithm to distribute the anti-roll torque of front and rear stabilizer bar dynamically, which can improve vehicle yaw dynamics response. The actuator controller is used for realizing the closed-loop control of the actuators displacement and generating the accurate anti-roll torque. The hardware-in-the-loop simulation platform is established based on AutoBox and active stabilizer bar actuators. The hardware-in-the-loop experiment is carried out under typical maneuvers. Experimental results show that the proposed control algorithm improves the vehicle roll and yaw dynamics response, which can enhance the vehicle roll stability, yaw stability, and ride comfort.

Influence of Road Excitation and Steering Wheel Input on Vehicle System Dynamic Responses

Considering the importance of increasing driving safety, the study of safety is a popular and critical topic of research in the vehicle industry. Vehicle roll behavior with sudden steering input is a main source of untripped rollover. However, previous research has seldom considered road excitation and its coupled effect on vehicle lateral response when focusing on lateral and vertical dynamics. To address this issue, a novel method was used to evaluate effects of varying road level and steering wheel input on vehicle roll behavior. Then, a 9 degree of freedom (9-DOF) full-car roll nonlinear model including vertical and lateral dynamics was developed to study vehicle roll dynamics with or without of road excitation. Based on a 6-DOF half-car roll model and 9-DOF full-car nonlinear model, relationship between three-dimensional (3-D) road excitation and various steering wheel inputs on vehicle roll performance was studied. Finally, an E-Class (SUV) level car model in CARSIM® was used, as a benchmark, with and without road input conditions. Both half-car and full-car models were analyzed under steering wheel inputs of 5°, 10° and 15°. Simulation results showed that the half-car model considering road input was found to have a maximum accuracy of 65%. Whereas, the full-car model had a minimum accuracy of 85%, which was significantly higher compared to the half-car model under the same scenario.

Design of an integrated control system to enhance vehicle roll and lateral dynamics

Besides lateral instability, one major threat to all ground vehicles, especially SUVs, is the danger of rollover during cornering. A coordination strategy based on fuzzy logic has been devised to coordinate the sub-controls; namely, active steering, active differential, active brake and a novel active roll control system. Independent study of each sub-control as well as an analysis of their inter-relationship has been carried out. The coordination strategy is supposed to resolve the conflict among control targets – which are sideslip regulation, yaw rate tracking, lateral acceleration tracking and roll motion control – all of which are to be done while maintaining the driver’s desired longitudinal acceleration. Thus, a compromise must be reached. Vehicle sideslip angle and yaw rate were considered to be the criteria for lateral stability; and a combination of roll angle, roll rate and lateral load transfer was selected as the criterion for roll stability. The results of simulations on two SUV models in CarSim software indicate that the integrated controller can successfully restore vehicles’ stability in critical condition.

Design and Evaluation of a Hierarchical Control Algorithm for an Electric Active Stabilizer Bar System

This paper describes a novel hierarchical control algorithm for the electric active stabilizer bar (ASB) system, which is applied to a four-wheeled road vehicle. The proposed control algorithm is designed to improve vehicle roll and yaw dynamics. The upper-level controller calculates the target active anti-roll torque via sliding mode control, which is aimed at improving the roll stability. The middle-level controller distributes active anti-roll torque between front and rear axle via fuzzy control, which can improve the handling stability through changing lateral load transfer of front and rear axles in real time. The lower level controller is employed to control the output torque of ASB actuators via PI control and improve the output characteristic of actuators with excellent response and stability. The numerical simulation and hardware-in-the-loop (HIL) experiment are carried out to evaluate the performance of the proposed control algorithm. It is demonstrated that the ASB system based on proposed control algorithm makes a significant improvement in the vehicle roll stability, ride comfort and handling stability.

WITHDRAWN: Stable switched tracking of vehicle roll dynamics using active suspension actuators

The article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause.The Full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy .

Model-Based Estimation for Vehicle Dynamics States at the Limit Handling

This technical brief proposes a new model-based estimation method for the vehicle sideslip angle, yaw rate, roll angle, and roll rate using unscented Kalman filter (UKF). Since a vehicle wheel could potentially lift off the ground during the limit handling, a switched vehicle roll dynamics model (wheel lift and no wheel lift) is developed and integrated within the proposed model-based estimation approach considering the availability of wheel speed sensor. The simulation results and analyses demonstrate the performance enhancement of the proposed estimation method over the method not considering wheel lift during the limit handling.

Detection of impending vehicle rollover with road bank angle consideration using a robust fuzzy observer

This article describes a method of vehicle dynamics estimation for impending rollover detection. We estimate vehicle dynamic states in presence of the road bank angle as a disturbance in the vehicle model using a robust observer. The estimated roll angle and roll rate are used to compute the rollover index which is based on the prediction of the lateral load transfer. In order to anticipate rollover detection, a new method is proposed to compute the time to rollover (TTR) using the load transfer ratio (LTR). The nonlinear model, deduced from the vehicle lateral and roll dynamics, is represented by a Takagi-Sugeno (T-S) fuzzy model. This representation is used to account for the nonlinearities of lateral cornering forces. The proposed T-S observer is designed with unmeasurable premise variables to cater for non-availability of the slip angles measurement. The proposed approach is evaluated using CarSim simulator under different driving scenarios. Simulation results show good efficiency of the proposed T-S observer and the rollover detection method.

A structured filter for Markovian switching systems

In this work, a new methodology for the structuring of multiple model estimation schemas is developed. The proposed filter is applied to the estimation and detection of active mode in dynamic systems. The discrete-time Markovian switching systems represented by several linear models, associated with a particular operating mode, are studied. Therefore, the main idea of this work is the subdivision of the models set to some subsets in order to improve the detection and estimation performances. Each subset is associated with sub-estimators based on models of the subset. In order to compute the global estimate and subset probabilities, a global estimator is proposed. Theoretical developments based on a hierarchical decision, leading to more efficiency in detection and state estimation, are proposed. Naturally, these results can be used for fault detection and isolation, using the activation probabilities of operating modes. These results are applied to detect switches in the centre of gravity for vehicle roll dynamics.

Study on the Time Lag between Steering Input and Vehicle Lateral Acceleration Response under Different Key Vehicle Parameters

A passively suspended road vehicle rolls outwards under the influence of lateral acceleration when cornering, which is very dangerous under large lateral acceleration. In this paper, time lag between steering input and vehicle lateral acceleration response is systematically studied to implement the active roll control algorithm from the viewpoint of vehicle system dynamics. A 3 DOF yaw-roll vehicle model is established based on vehicle lateral, roll and yaw dynamics. Vehicle parameters of a 1997 Jeep Cherokee is used for parametric study, where the influences of vehicle velocity, steering frequency, mass, length, roll, yaw moment of inertia, position of vehicle centre of gravity, and tyre cornering stiffness are studied via numerical simulation. The analysis results will help improve the real time rollover warning/control algorithm design for vehicle safety.

Robust Vehicle Roll Dynamics Identification based on Roll Rate Measurements

The modern vehicle development process is required to provide short product update cycles and high cost efficiency. Nonetheless, the demands on vehicle safety and handling quality remain high as ever. Driving characteristics evaluation relies on extensive subjective assessment as well as objective methods. Model-based objective assessment saves time and costs as most of the evaluation work can be done offline, i.e. during or after a test run the parameters of a vehicle dynamics model are identified and used for later simulation work. Referring to vehicle dynamics assessment the roll movement of the vehicle is of great importance (aside from longitudinal and lateral vehicle dynamics).This paper presents two methods for the online parameter identification of a simple roll dynamics model. These parameters can then be used for computation of the roll angle. The first method employs a recursive least-squares algorithm whereas the second method consists of a sliding-mode observer combined with an uncertainty reconstruction technique. Compared to other existing methods for vehicle roll dynamics identification both estimation techniques only require the cost-efficiently measurable roll rate and lateral acceleration signals.

Improvement of vehicle roll stability by varying suspension properties

Vehicle roll dynamics are strongly influenced by suspension properties such as roll centre height, roll steer, and roll camber. In this paper, the effects of suspension properties on vehicle roll response have been investigated using a multi-body vehicle dynamics programme. Roll dynamics of a vehicle model with MacPherson (front) and multilink (rear) suspensions were evaluated for the fishhook manoeuvre and variations of its roll response due to changes in the suspension properties were assessed by quantitatively analysing the vehicle response through simulation. Critical suspension design parameters for vehicle roll dynamics were identified and adjusted to improve roll stability of the vehicle model with passive suspension. Design of experiments has been used for identifying critical hardpoints affecting the suspension parameters, and optimisation techniques were employed for parameter optimisation. This approach provides a viable alternative to costlier active control systems for economy-class vehicles.