Direct yaw-moment control of an in-wheel-motored electric vehicle based on body slip angle fuzzy observer

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 settin

Real-Time Vehicle Parameter Estimation and Adaptive Stability Control

This dissertation presents a novel Electronic Stability Control (ESC) strategy that is capable of adapting to changing vehicle mass, tire condition and road surface conditions. The benefits of ESC are well understood with regard to assisting drivers to maintain vehicle control during extreme handling maneuvers or when extreme road conditions such as ice are encountered. However state of the art ESC strategies rely on known and invariable vehicle parameters such as vehicle mass, yaw moment of inertia and tire cornering stiffness coefficients. Such vehicle parameters may change over time, especially in the case of heavy trucks which encounter widely varying load conditions. The objective of this research is to develop an ESC control strategy capable of identifying changes in these critical parameters and adapting the control strategy accordingly. An ESC strategy that is capable of identifying and adapting to changes in vehicle parameters is presented. The ESC system utilizes the same sensors and actuators used on commercially-available ESC systems. A nonlinear reduced-order observer is used to estimate vehicle sideslip and tire slip angles. In addition, lateral forces are estimated providing a real-time estimate of lateral force capability of the tires with respect to slip angle. A recursive least squares estimation algorithm is used to automatically identify tire cornering stiffness coefficients, which in turn provides a real-time indication of axle lateral force saturation and estimation of road/tire coefficient of friction. In addition, the recursive least squares estimation is shown to identify changes in yaw moment of inertia that may occur due to changes in vehicle loading conditions. An algorithm calculates the reduction in yaw moment due to axle saturation and determines an equivalent moment to be generated by differential braking on the opposite axle. A second algorithm uses the slip angle estimates and vehicle states to predict a Time to Saturation (TTS) value of the rear axle and takes appropriate action to prevent vehicle loss of control. Simulation results using a high fidelity vehicle modeled in CarSim show that the ESC strategy provides improved vehicle performance with regard to handling stability and is capable of adapting to the identified changes in vehicle parameters

Development of Urban Electric Bus Drivetrain

The development of the drivetrain for a new series of urban electric buses is presented in the paper. The traction and design properties of several drive variants are compared. The efficiency of the drive was tested using simulation calculations of the vehicle rides based on data from real bus lines in Prague. The results of the design work and simulation calculations are presented in the paper

Tyre curve estimation in slip-controlled braking

Progress in reducing actuator delays in pneumatic brake systems creates an opportunity for advanced anti-lock braking algorithms to be used on heavy goods vehicles. However, these algorithms require knowledge of variables that are impractical to measure directly. This paper introduces a braking force observer and road surface identification algorithms to support a sliding-mode slip controller for air-braked heavy vehicles. Both the force observer and the slip controller are shown to operate robustly under a variety of conditions in quarter-car simulations. A non-linear least-squares algorithm was found to be capable of performing regressions on all the parameters of the tyre model from the University of Michigan Transportation Research Institute when used ‘in the loop’ with the controller and the observer. A recursive least-squares algorithm that is less computationally expensive than the non-linear algorithm was also investigated but gave only reasonable estimates of the tyre model parameters on high-friction smooth roads. The authors would like to thank the members of the Cambridge Vehicle Dynamics Consortium (CVDC), and the Gates Cambridge Trust for their parts in funding this work.This is the author accepted manuscript. The final version is available from Sage via http://dx.doi.org/10.1177/095440701558593

Nonparametric identification of linearizations and uncertainty using Gaussian process models – application to robust wheel slip control

Gaussian process prior models offer a nonparametric approach to modelling unknown nonlinear systems from experimental data. These are flexible models which automatically adapt their model complexity to the available data, and which give not only mean predictions but also the variance of these predictions. A further advantage is the analytical derivation of derivatives of the model with respect to inputs, with their variance, providing a direct estimate of the locally linearized model with its corresponding parameter variance. We show how this can be used to tune a controller based on the linearized models, taking into account their uncertainty. The approach is applied to a simulated wheel slip control task illustrating controller development based on a nonparametric model of the unknown friction nonlinearity. Local stability and robustness of the controllers are tuned based on the uncertainty of the nonlinear models’ derivatives

On the vehicle sideslip angle estimation: a literature review of methods, models and innovations

Typical active safety systems controlling the dynamics of passenger cars rely on real-time monitoring of the vehicle sideslip angle (VSA), together with other signals like wheel angular velocities, steering angle, lateral acceleration, and the rate of rotation about the vertical axis, known as the yaw rate. The VSA (aka attitude or “drifting” angle) is defined as the angle between the vehicle longitudinal axis and the direction of travel, taking the centre of gravity as a reference. It is basically a measure of the misalignment between vehicle orientation and trajectory therefore it is a vital piece of information enabling directional stability assessment, in transients following emergency manoeuvres for instance. As explained in the introduction the VSA is not measured directly for impracticality and it is estimated on the basis of available measurements like wheel velocities, linear and angular accelerations etc. This work is intended to provide a comprehensive literature review on the VSA estimation problem. Two main estimation methods have been categorised, i.e. Observer-based and Neural Network-based, focusing on the most effective and innovative approaches. As the first method normally relies on a vehicle model, a review of the vehicle models has been included. Advantages and limitations of each technique have been highlighted and discussed