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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 exten...
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Citations
... Sideslip angle estimation based on kinematics primarily involves directly integrating the sensor signals or constructing an estimator via GPS/INS. Li et al. [10] compared the direct integration method with other estimation techniques in practical experiments. Bevly et al. [11] built a classic bicycle model and combined INS with GPS measurements to obtain superior accuracy of the sideslip angle. ...
... However, this kind of estima- Sideslip angle estimation based on kinematics primarily involves directly integrating the sensor signals or constructing an estimator via GPS/INS. Li et al. [10] compared the direct integration method with other estimation techniques in practical experiments. Bevly et al. [11] built a classic bicycle model and combined INS with GPS measurements to obtain superior accuracy of the sideslip angle. ...
An accurate and reliable sideslip angle is crucial for active safety control systems and advanced driver-assistance systems (ADAS). The direct measurement method of the sideslip angle suffers from challenges of high costs and environmental sensitivity, so sideslip angle estimation has always been a significant research issue. To improve the precision and robustness of sideslip angle estimation for distributed drive electric vehicles (DDEV) in extreme maneuvering scenarios, this paper presents a novel robust unscented particle filter (RUPF) algorithm based on low-cost onboard sensors. Firstly, a nonlinear dynamics model of DDEV is constructed, providing a theoretical foundation for the design of the RUPF algorithm. Then, the RUPF algorithm, which incorporates the unscented Kalman filter (UKF) to update importance density and utilizes systematic random resampling to mitigate particle degradation, is designed for estimation. Eventually, the availability of the proposed RUPF algorithm is validated on the co-simulation platform with non-Gaussian noises. Simulation results demonstrate that RUPF algorithm attains a higher precision and stronger robustness compared with the traditional PF and UKF algorithms.
... In [35], an adaptive event-triggering condition is designed while ensuring that the Zeno behavior is avoided. In [36,37], an active suspension is controlled through an event-triggered H ∞ controller in a networked control system with communication delays. Nevertheless, a significant portion of these results are based on simulations, making it difficult to find hard experimental evidence that validates the effectiveness of event-driven systems. ...
In recent years, there has been a significant integration of advanced technology into the automotive industry, aimed primarily at enhancing safety and ride comfort. While a notable proportion of these driver-assist systems focuses on skid prevention, insufficient attention has been paid to addressing other crucial scenarios, such as rollovers. The accurate estimation of slip and roll angles plays a vital role in ensuring vehicle control and safety, making these parameters essential, especially with the rise of modern technologies that incorporate networked communication and distributed computing. Furthermore, there exists a lag in the transmission of information between the various vehicle systems, including sensors, actuators, and controllers. This paper outlines the design of an IoT architecture that accurately estimates the sideslip angle and roll angle of a vehicle, while addressing network transmission delays with a networked control system and an event-triggered communication scheme. Experimental results are presented to validate the performance of the IoT architecture proposed. The event-triggered scheme of the IoT solution is used to decrease data transmission and prevent network overload.
... The major goal of the sideslip angle rate feedback technique is to correct model errors brought on by inaccurate road friction estimation, whereas the main goal of the damping item is to prevent the build-up of errors. The estimated results are juxtaposed with the actual values obtained from the differential Global Positioning System (GPS) on a lowfriction road [30]. In order to estimate the sideslip angle, a novel observer based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) and Kalman filters is proposed in a study. ...
With the growing interest in self-driving vehicles, safety in vehicle driving is becoming an increasingly important aspect. The sideslip angle is a key quantity for modern control systems that aim to improve passenger safety. It directly affects the lateral motion and stability of a vehicle. In particular, a large sideslip angle can cause the vehicle to experience oversteer or understeer, which can lead to loss of control and potentially result in an accident. For this reason, it is necessary to constantly monitor this quantity while driving in order to implement appropriate action if necessary. Sensors that directly measure this quantity are expensive and difficult to implement. In this paper, two neural networks to estimate the sideslip angle are proposed. The quantities that most influence the vehicle’s sideslip angle were assessed. Furthermore, the neural networks can exploit data from previous instants of time for estimation purposes. In particular, the first uses lateral acceleration and steering wheel angle as input, the second uses longitudinal acceleration, lateral acceleration and yaw rate. Experimental tests carried out on manoeuvres that stimulate the sideslip angle have shown that, although the estimators use few measures, they are able to accurately estimate the quantity of interest.
... For instance, Dakhlallah et al. propose a sideslip angle estimator based on the Dugoff tire forces model, using the EKF approach [62]. Meanwhile, Li et al. use the sideslip angle rate as the feedback measurement and design an EKF based on steering torque to achieve a speedy response in estimating the sideslip angle [63]. However, the assumption of Gaussian noise in EKF may introduce extra estimation errors. ...
Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at the state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration.
... When the tire is running in a linear region, linear Kalman filtering is sufficient, but its estimation effect deteriorates when the vehicle is driving in certain extreme conditions. In this case, most of the literature selects nonlinear tire models such as the brush model [14], Magic Formula of Pacejka [15], Arctangent tire model [16], and extended Kalman filter (EKF) and unscented Kalman filter (UKF) based on nonlinear vehicle models, which exhibit better estimation results [17][18][19]. A high degree of freedom vehicle dynamics model was established, taking into account lateral and longitudinal forces and road adhesion factors, using existing onboard sensors for real-time estimation. ...
Most existing ESC (electronic stability control) and ADS (auto drive system) stability controls rely on the measurement of yaw rate and sideslip angle. However, the existing sensors are too expensive, which is one of the factors that makes it difficult to measure the side slip angle of vehicles directly. Therefore, the estimation of sideslip angle has been extensively discussed in the relevant literature. Accurate modeling is complicated by the fact that vehicles are highly nonlinear. This article combines a radial basis function neural network with an unscented Kalman filter to propose a new sideslip angle estimation method for controlling the dynamic behavior of vehicles. Considering the influence of input data type and sensor ease of measurement factors on the results, a two-degrees-of-freedom vehicle nonlinear dynamic model was established, and a radial basis function neural network estimation algorithm was designed. In order to reduce the impact of noise and improve the reliability of the algorithm, the neural network algorithm was combined with the Kalman filter. The information collected from low-cost sensors for actual vehicle operation (longitudinal vehicle speed, steering wheel angle, yaw rate, lateral acceleration) was trained using a radial basis function neural network to obtain a “pseudo slip angle”. The “pseudo slip angle”, yaw rate, and lateral acceleration are input as observations of the Kalman filter. The sideslip angle obtained from different observation methods was compared with the values provided by the Carsim 2020. The experiment shows that the sideslip angle estimator based on the radial basis function neural network and unscented Kalman filter achieves the optimal effect.
... However, due to the strong nonlinearity of the problem and the high sensitivity of most methods to parameter variability and sensor noise, determining the side-slip angle is still considered to be a nontrivial task. The most common methodology is the extended Kalman filter (EKF) [14,15]. In the literature different forms and applications of the Kalman filter can be found, for example, in combination with fuzzy logic [16], with the Pacejka's magic formula [17], or in other hybrid forms [18]. ...
div>A valuable quantity for analyzing the lateral dynamics of road vehicles is the side-slip angle, that is, the angle between the vehicle’s longitudinal axis and its speed direction. A reliable real-time side-slip angle value enables several features, such as stability controls, identification of understeer and oversteer conditions, estimation of lateral forces during cornering, or tire grip and wear estimation. Since the direct measurement of this variable can only be done with complex and expensive devices, it is worth trying to estimate it through virtual sensors based on mathematical models. This article illustrates a methodology for real-time on-board estimation of the side-slip angle through a machine learning model (SSE—side-slip estimator). It exploits a recurrent neural network trained and tested via on-road experimental data acquisition. In particular, the machine learning model only uses input signals from a standard road car sensor configuration. The model adaptability to different road conditions and tire wear levels has been verified through a sensitivity analysis and model testing on real-world data proves the robustness and accuracy of the proposed solution achieving a root mean square error (RMSE) of 0.18 deg and a maximum absolute error of 1.52 deg on the test dataset. The proposed model can be considered as a reliable and cheap potential solution for the real-time on-board side-slip angle estimation in serial cars.</div
... Article history: received on Nov. 16 During the past decade, lots of researchers have been engaged in the field of sideslip angle estimation, achieving some great results. The algorithms applied to sideslip angle observer design in prior studies can be classified as the Kalman-filter-based method [11][12][13][14][15][16][17][18], the nonlinearobserver-based method [19][20][21][22][23][24], the optimal estimation method [25][26][27], the information fusion estimation method [27][28][29][30][31][32], the robust estimation method [34][35][36] et al. ...
... Article history: received on Nov. 16 During the past decade, lots of researchers have been engaged in the field of sideslip angle estimation, achieving some great results. The algorithms applied to sideslip angle observer design in prior studies can be classified as the Kalman-filter-based method [11][12][13][14][15][16][17][18], the nonlinearobserver-based method [19][20][21][22][23][24], the optimal estimation method [25][26][27], the information fusion estimation method [27][28][29][30][31][32], the robust estimation method [34][35][36] et al. The Kalman filter and the corresponding improved filter are widely used in the research on sideslip angle estimation. ...
... Considering the longitudinal force and sideslip angle are difficult and costly to be measured by the on-board vehicle sensors, the design of estimators for longitudinal force and sideslip angle estimation is essential. The existing studies present different forms of observer designs for vehicle state estimation, and the algorithms used for observer design in prior papers can adopt a Kalman-filter-based method [15][16][17][18][19], a nonlinear-observer-based method [20][21][22][23][24], an optimal estimation method [25][26][27][28][29], an information fusion estimation method [17,19,[29][30][31][32][33], a robust estimation method [34][35][36][37] et al. The Kalman filter is widely used in vehicle state estimation and recently, on the basis of characteristics of the objects to be estimated, researchers approach to designing the vehicle state estimators applying a fusion of Kalman filter with another advanced estimation theory. ...
... In [30,31], studies on estimating the vehicle state based on tire force information have also been reported successively. Moreover, a variable structure EKF that takes into account the model error was proposed to further improve the estimation performance [32]. To improve the computational performance of EKF, Guo et al. [33] designed a deployment scheme based on a field programmable gate array, and test results showed that it has high computational efficiency. ...
div>The vehicle dynamic state is essential for stability control and decision-making of intelligent vehicles. However, these states cannot usually be measured directly and need to be obtained indirectly using additional estimation algorithms. Unfortunately, most of the existing estimation methods ignore the effect of data loss on estimation accuracy. Furthermore, high-order filters have been proven that can significantly improve estimation performance. Therefore, a second-order fault-tolerant extended Kalman filter (SOFTEKF) is designed to predict the vehicle state in the case of data loss. The loss of sensor data is described by a random discrete distribution. Then, an estimator of minimum estimation error covariance is derived based on the extended Kalman filter (EKF) framework. Finally, experimental tests demonstrate that the SOFTEKF can reduce the effect of data loss and improve estimation accuracy by at least 10.6% compared to the traditional EKF and fault-tolerant EKF.</div
... Therefore, in practical applications, the estimation of VSA has become a key and difficult point of research on stability control. Various estimation algorithms have been proposed for VSA, including direct integration [4], state observation based on a vehicle dynamics model [5][6][7][8][9][10][11], neural network methods [12][13][14][15][16], and the extended Kalman filter [5,[17][18][19]. In summary, kinematics, dynamics, and fusion estimation methods are commonly used. ...
... Therefore, in practical applications, the estimation of VSA has become a key and difficult point of research on stability control. Various estimation algorithms have been proposed for VSA, including direct integration [4], state observation based on a vehicle dynamics model [5][6][7][8][9][10][11], neural network methods [12][13][14][15][16], and the extended Kalman filter [5,[17][18][19]. In summary, kinematics, dynamics, and fusion estimation methods are commonly used. ...
