A stabilizing observer-based control algorithm for an in-wheel-motored vehicle is proposed, which generates direct yaw moment to compensate for the state deviations. The control scheme is based on a fuzzy rule-based body slip angle (beta) observer. In the design strategy of the fuzzy observer, the vehicle dynamics is represented by Takagi-Sugeno-like fuzzy models. Initially, local equivalent vehicle models are built using the linear approximations of vehicle dynamics for low and high lateral acceleration operating regimes, respectively. The optimal beta observer is then designed for each local model using Kalman filter theory. Finally, local observers are combined to form the overall control system by using fuzzy rules. These fuzzy rules represent the qualitative relationships among the variables associated with the nonlinear and uncertain nature of vehicle dynamics, such as tire force saturation and the influence of road adherence. An adaptation mechanism for the fuzzy membership functions has been incorporated to improve the accuracy and performance of the system. The effectiveness of this design approach has been demonstrated in simulations and in a real-time experimental setting.

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Direct Yaw-Moment Control of an In-Wheel-Motored Electric Vehicle Based on Body Slip Angle Fuzzy Observer

Robust estimation of vehicle states (e.g., vehicle sideslip angle and roll angle) is essential for vehicle stability control applications such as yaw stability control and roll stability control. This paper proposes novel methods for estimating sideslip angle and roll angle using real-time lateral tire force measurements, obtained from the multisensing hub units, for practical applications to vehicle control systems of in-wheel-motor-driven electric vehicles. In vehicle sideslip estimation, a recursive least squares (RLS) algorithm with a forgetting factor is utilized based on a linear vehicle model and sensor measurements. In roll angle estimation, the Kalman filter is designed by integrating available sensor measurements and roll dynamics. The proposed estimation methods, RLS-based sideslip angle estimator, and the Kalman filter are evaluated through field tests on an experimental electric vehicle. The experimental results show that the proposed estimator can accurately estimate the vehicle sideslip angle and roll angle. It is experimentally confirmed that the estimation accuracy is improved by more than 50% comparing to conventional method's one (see rms error shown in Fig. 4). Moreover, the feasibility of practical applications of the lateral tire force sensors to vehicle state estimation is verified through various test results.

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Estimation of Sideslip and Roll Angles of Electric Vehicles Using Lateral Tire Force Sensors Through RLS and Kalman Filter Approaches

Sideslip angle plays an important role in vehicle stability control. However, it is difficult to measure directly unless some complex and expensive devices are employed. Thus, sideslip angle estimated by vehicle states such as lateral acceleration, yaw rate and so on is required in real-time vehicle stability control. A new variable structure extended Kalman filter integrated with the sideslip angle rate feedback algorithm and damping item is proposed in this paper. The main idea of the sideslip angle rate feedback algorithm is to compensate the model error caused by road friction estimation inaccuracy while that of the damping item is to suppress the error accumulation. The estimated results are compared with the real values obtained by the differential global position system on a low friction road and it shows that the proposed methodology can provide accurate sideslip angle to some extent for vehicle stability control.

A variable structure extended Kalman filter for vehicle sideslip angle estimation on a low friction road

Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the recent developments in the estimation of vehicle dynamic states. The definitions used in vehicle dynamic state estimation are first introduced, and alternative estimation structures are presented. Then, the sensor configuration schemes used to estimate vehicle velocity, sideslip angle, yaw rate and roll angle are presented. The vehicle models used for vehicle dynamic state estimation are further summarized, and representative estimation approaches are discussed. Future concerns and perspectives for vehicle dynamic state estimation are also discussed.

Vehicle dynamic state estimation: state of the art schemes and perspectives

In this article, an adaptive integrated control algorithm based on active front steering and direct yaw moment control using direct Lyapunov method is proposed. Variation of cornering stiffness is considered through adaptation laws in the algorithm to ensure robustness of the integrated controller. A simple two degrees of freedom (DOF) vehicle model is used to develop the control algorithm. To evaluate the control algorithm developed here, a nonlinear eight-DOF vehicle model along with a combined-slip tyre model and a single-point preview driver model are used. Control commands are executed through correction steering angle on front wheels and braking torque applied on one of the four wheels. Simulation of a double lane change manoeuvre using Matlab®/Simulink is used for evaluation of the control algorithm. Simulation results show that the integrated control algorithm can significantly enhance vehicle stability during emergency evasive manoeuvres on various road conditions ranging from dry asphalt to very slippery packed snow road surfaces.

An adaptive integrated algorithm for active front steering and direct yaw moment control based on direct Lyapunov method

Sideslip angle could provide important information concerning vehicle's stability. Unfortunately direct measurement of sideslip angle requires a complex and expensive experimental set-up, which is not suitable for implementation on ordinary passenger cars; thus, this quantity has to be estimated starting from the measurements of vehicle lateral/longitudinal acceleration, speed, yaw rate and steer angle. According to the proposed methodology, sideslip angle is estimated as a weighted mean of the results provided by a kinematic formulation and those obtained through a state observer based on vehicle single-track model. Kinematical formula is considered reliable for a transient manoeuvre, while the state observer is used in nearly quasi-state condition. The basic idea of the work is to make use of the information provided by the kinematic formulation during a transient manoeuvre to update the single-track model parameters (tires cornering stiffnesses). A fuzzy-logic procedure was implemented to identify stea...

M. Pesce

Papers

A methodology for vehicle sideslip angle identification: comparison with experimental data