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In this study, two novel control methods are theoretically introduced as direct slip angle control (DSAC) and model predictive direct slip angle control (MPDSAC) for rotor side converter (RSC) of the standalone Doubly‐Fed Induction Generator (DFIG)‐DC system (S‐DFIG‐DC). The numerical analysis of the proposed methods demonstrated that the proposed DSAC method could reduce torque ripple and current harmonics, decrease the output DC voltage drop in fast changes of load condition, and have faster dynamic response. Also, the MPDSAC method could further diminish torque and flux ripple, current harmonics, and output DC voltage drop in sharp changes of load condition compared with the presented DSAC and direct torque control (DTC) methods. The sensitivity analysis of the proposed control methods is investigated at different operating conditions. The performance and usefulness of the DSAC and MPDSAC schemes are verified by various experiments and compared with the conventional DTC method. The block diagram of control method and experimental setup.

The slip angle and attitude are vital for automated driving. In this paper, a systematic inertial measurement unit (IMU)-based vehicle slip angle and attitude estimation method aided by vehicle dynamics is proposed. This method can estimate the slip angle and attitude simultaneously and autonomously. With accurate attitude, the slip angle can be estimated precisely even though the vehicle dynamic model (VDM)-based velocity estimator diverges for a short time. First, the longitudinal velocity, pitch angle, lateral velocity, and roll angle were estimated by two estimators based on VDM considering the lever arm between the IMU and rotation center. When this information was in high fidelity, it was applied to aid the IMU-based slip angle and attitude estimators to eliminate the accumulated error correctly. Since there is a time delay in detecting the abnormal estimation results from VDM-based estimators during critical steering, a novel delay estimation and prediction structure was proposed to avoid the outlier feedback from vehicle dynamics estimators for the IMU-based slip angle and attitude estimators. Finally, the proposed estimation method was validated under large lateral excitation experimental tests including double lane change (DLC) and slalom maneuvers.

Nowadays, the active safety systems that control the dynamics of passenger cars usually rely on real-time monitoring of vehicle side-slip angle (VSA). The VSA can’t be measured directly on the production vehicles since it requires the employment of high-end and expensive instrumentation. To realiably overcome the VSA estimation problem, different model-based techniques can be adopted. The aim of this work is to compare the performance of different model-based state estimators, evaluating both the estimation accuracy and the computational cost, required by each algorithm. To this purpose Extended Kalman Filters, Unscented Kalman Filters and Particle Filters have been implemented for the vehicle system under analysis. The physical representation of the process is represented by a single-track vehicle model adopting a simplified Pacejka tyre model. The results numerical results are then compared to the experimental data acquired within a specifically designed testing campaign, able to explore the entire vehicle dynamic range. To this aim an electric go-kart has been employed as a vehicle, equipped with steering wheel encoder, wheels angular speed encoder and IMU, while an S-motion has been adopted for the measurement of the experimental VSA quantity.

Tires equipped with sensors, the so-called “intelligent tires”, can provide vital information for control systems, drivers and external users. In this research, tire dynamic strain characteristics in cornering conditions are collected and analysed in relation to the variation of tire working conditions, such as inflation pressure, rolling speed, vertical load and slip angle. An experimental tire strain-based prototype and an indoor tire test rig are used to demonstrate the suitability of strain sensors to establish relations between strain data and lateral force. The results of experiments show that strain values drop sharply when lateral force is decreasing, which can be used to predict tire slip conditions. As a first approach to estimate some tire working conditions, such as the slip angle and vertical load, a fuzzy logic method has been developed. The simulation and test results confirm the feasibility of strain sensors and the proposed computational model to solve the non-linearity characteristics of the tires’ parameters and turn tires into a source of useful information.

Islanding detection needs are becoming a pivotal constituent of the power system, since the penetration of distributed generators in the utility power system is continually increasing. Accurate threshold setting is an integral part of the island detection scheme since an inappropriate threshold might cause a hazardous situation. This study looked at the islanding conditions as well as two transient faults, such as a single line to ground fault and a three-phase balance fault, to assess the event distinguishing ability of the proposed method. Therefore, the goal of this research was to determine the threshold of the island if the distributed generator (DG) capacity is greater than the connected feeder load, which is the over-frequency island condition, and if the DG capacity is less than the connected feeder load, which is the under-frequency island condition. The significance of this research work is to propose a new island detection threshold setting method using the slip angle and acceleration angle that comes from phasor measurement unit (PMU) voltage angle data. The proposed threshold setting method was simulated in the PowerWorld simulator on a modified IEEE 30 bus system equipped with DG. There are three different interconnection scenarios in the test system and the performance of the proposed method shows that getting the island threshold for all the scenarios requires a single time step or 20 mile seconds after incepting an island into the network. In addition, it can distinguish between the real islanding threshold and the transient faults threshold.

The possibility of using tires as active sensors opens the door to a huge number of different ways to accomplish this goal. In this case, based on a tire equipped with strain sensors, also known as an Intelligent Tire, relevant vehicle dynamics information can be provided. The purpose of this research is to improve the strain-based methodology for Intelligent Tires to estimate all tire forces, based only on deformations measured in the contact patch. Firstly, through an indoor test rig data, an algorithm has been developed to pick out the relevant features of strain data and correlate them with tire parameters. This information of the tire contact patch is then transmitted to a fuzzy logic system to estimate the tire parameters. To evaluate the reliability of the proposed estimator, the well-known simulation software CarSim has been used to back up the estimation results. The software CarSim has been used to provide the vehicle parameters in complex maneuvers. Finally, the estimations have been checked with the simulation results. This approach has enabled the behaviour of the intelligent tire to be tested for different maneuvers and velocities, providing key information about the tire parameters directly from the only contact that exists between the vehicle and the road.

To enhance vehicle maneuverability and stability, a controller with 4-wheel steering (4WS), electronic stability control (ESC) and a torque vectoring device (TVD) under constraint on the front slip angles is designed in this research. In the controller, the control allocation method is adopted to generate yaw moment via 4WS, ESC and TVD. If the front steering angle is added for generating yaw moment, the steering performance of the vehicle can be further deteriorated. This is because the magnitude of the lateral tire forces are limited and the required yaw moment is insufficient. Constraint is imposed on the magnitude of the front slip angles in order to prevent the lateral tire forces from saturating. The driving simulation is performed by considering the limit of the front slip angle proposed in this study. Compared to the case that uses the existing 4WS, the results of this study are derived from the actuator combination that enhances performance while maintaining stability.

Wheel alignment of a vehicle composed of toe, camber and caster is essential for stable driving. Among them, the toe angle can be easily adjusted in many commercial vehicles when misaligned. However, there have been many difficulties for a driver to directly detect the misalignment of the toe angle. To solve this problem, this paper proposes a novel system that detects misaligned toe angle in real-time by utilizing the lateral tire force and tire aligning moment. The system is largely divided into the lateral tire force model construction, tire aligning moment model construction, and misalignment detection. During the lateral tire force model and the tire aligning moment model construction, linearized recursive least squares are used to identify parameters necessary for the building of the models. Afterwards, during the misalignment detection, the misaligned toe angle is detected in real-time without additional sensors by estimating the slip angle of each wheel reflecting the toe angle effect based on these two models. The proposed system is verified by the vehicle dynamics software CarSim, and the simulation results show that misaligned toe angle can be successfully detected in real-time while driving.

Wheel-slippage is a crucial but one of the marginally attended subjects regarding indoor navigation. Uncompensated slippage has the potential to introduce serious consequences in the form of safety-violation while manipulating obstacles and degraded performance when the vehicle is subjected to tracking and interception. This paper aims at establishing an alternative approach to dynamic modeling and robust control by proposing online estimation of slip parameters and modifying the kinematic model such that it is capable to accommodate slip-disturbance inputs. This approach works in a minimally invasive way, without interfering with or replacing the existing controller. The proposed approach has a low computational requirement and can be easily implemented without any major changes in the control architecture.

Real-time estimation of vehicle sideslip angle is essential for both safety and performance applications. This study presents a temperature-adaptive Extended Kalman Filter (EKF) that estimates the sideslip angle of a racing vehicle by integrating dynamic and kinematic information. A temperature-dependent Pacejka tire model, derived directly from track tests, is embedded in a 3-degree-of-freedom dual-track vehicle model and used within the EKF to compensate for temperature-induced variations in tire behavior. The adaptive model parameters are identified from standard on-track maneuvers conducted at different tire temperatures, without the need for additional indoor rig testing. Experimental validation on a race track demonstrates that incorporating tire temperature adaptation and combining dynamic and kinematic estimation significantly enhance estimation accuracy, particularly underow-grip and high-performance driving conditions attested by a reduction of 40–50% in RMS error and a further reduction in maximum absolute error.