Wheel Forces Estimation via Adaptive Sub-Optimal Second Order Sliding Mode Observers

In this work a system for the estimation of the forces (both longitudinal and lateral) exerted between the tires and the road is presented. Starting from two of the most commonly used descriptions of the vehicle dynamics, the single-corner and the single-track models, a system composed of Sub-Optimal Second Order Sliding Mode observers in a cascade structure plus an adaptive element is developed and verified to be effective in conditions in which the effect of the weight transfer can be neglected. One notable property of this approach is that only standard sensors, which are present in most of the stock cars, are exploited. The practical implementation is done using a switched/time-based adaptation law for the gains of the observers, in order to be able to track the quantities in a wide range of conditions while keeping the chattering low. Simulation results are presented in IPG Car-Maker

A literature survey on sideslip angle estimation using vehicle dynamics based methods

The vehicle sideslip angle or lateral velocity is a measure both for driving stability and for occupant’s subjective perception of safety. With the introduction of vehicle dynamics control systems and automated driving functions, knowledge of this vehicle motion state is required for many control strategies. This article gives an overview on the state of the art on sideslip angle estimation. In contrast to other literature studies on this topic, it focuses on vehicle dynamics based algorithms. The following types of observers are discussed: Kalman Filter-type, recursive least squares (RLS), sliding mode observers (SMO) or nonlinear observers (NLO). Eventually, cascaded observers are used that first estimate some states, which then act as input to the sideslip angle estimator. Since the choice of an observer strategy always depends on the application, this article provides a brief insight into the work of selected research groups that have studied the topic. These examples will help to clarify the presence of many different approaches in the literature. A detailed discussion on vehicle and tire models is not included but referenced to other sources. Finally, this article provides recommendations for two main target groups: First, researchers and engineers that plan to design an algorithm for sideslip angle estimation using deterministic vehicle dynamics based approaches. Second, researchers and engineers planning to include an existing algorithm in an automated driving function that want to learn about advantages and limitations of these types of algorithms. Author

Sliding Mode Measurement Feedback Control for Antilock Braking Systems

We describe a nonlinear observer-based design for control of vehicle traction that is important in providing safety and obtaining desired longitudinal vehicle motion. First, a robust sliding mode controller is designed to maintain the wheel slip at any given value. Simulations show that longitudinal traction controller is capable of controlling the vehicle with parameter deviations and disturbances. The direct state feedback is then replaced with nonlinear observers to estimate the vehicle velocity from the output of the system (i.e., wheel velocity). The nonlinear model of the system is shown locally observable. The effects and drawbacks of the extended Kalman filters and sliding observers are shown via simulations. The sliding observer is found promising while the extended Kalman filter is unsatisfactory due to unpredictable changes in the road condition

Smart Traction Control Systems for Electric Vehicles Using Acoustic Road-type Estimation

The application of traction control systems (TCS) for electric vehicles (EV) has great potential due to easy implementation of torque control with direct-drive motors. However, the control system usually requires road-tire friction and slip-ratio values, which must be estimated. While it is not possible to obtain the first one directly, the estimation of latter value requires accurate measurements of chassis and wheel velocity. In addition, existing TCS structures are often designed without considering the robustness and energy efficiency of torque control. In this work, both problems are addressed with a smart TCS design having an integrated acoustic road-type estimation (ARTE) unit. This unit enables the road-type recognition and this information is used to retrieve the correct look-up table between friction coefficient and slip-ratio. The estimation of the friction coefficient helps the system to update the necessary input torque. The ARTE unit utilizes machine learning, mapping the acoustic feature inputs to road-type as output. In this study, three existing TCS for EVs are examined with and without the integrated ARTE unit. The results show significant performance improvement with ARTE, reducing the slip ratio by 75% while saving energy via reduction of applied torque and increasing the robustness of the TCS.Comment: Accepted to be published by IEEE Trans. on Intelligent Vehicles, 22 Jan 201

Adaptive and Robust Braking-Traction Control Systems

The designs of commercial Anti-Lock Braking Systems often rely on assumptions of a torsionally rigid tire-wheel system and heavily rely on hub-mounted wheel speed sensors to manage tire-road slip conditions. However, advancements in high-bandwidth braking systems, in-wheel motors, variations in tire/wheel designs, and loss of inflation pressure, have produced scenarios where the tire\u27s torsional dynamics could be easily excited by the braking system actuator. In these scenarios, the slip conditions for the tire-belt/ring will be dynamically different from what can be inferred from the wheel speed sensors. This dissertation investigates the interaction of tire torsional dynamics with ABS & traction controllers and offers new control designs that incorporate schemes for identifying and accommodating these dynamics. To this end, suitable braking system and tire torsional dynamics simulation models as well as experimental test rigs were developed. It is found that, indeed, rigid-wheel based controllers give degraded performance when coupled with low torsional stiffness tires. A closed-loop observer/nonlinear controller structure is proposed that adapts to unknown tire sidewall and tread parameters during braking events. It also provides estimates of difficult to measure state variables such as belt/ring speed. The controller includes a novel virtual damper emulation that can be used to tune the system response. An adaptive sliding-mode controller is also introduced that combines robust stability characteristics with tire/tread parameter and state estimation. The sliding mode controller is shown to be very effective at tracking its estimated target, at the expense of reducing the tire parameter adaptation performance. Finally, a modular robust state observer is developed that allows for robust estimation of the system states in the presence of uncertainties and external disturbances without the need for sidewall parameter adaptation

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

A Modified HOSM Controller Applied to an ABS Laboratory Setup with Adaptive Parameter

The antilock braking system (ABS) is an electromechanical device whose controller is challenging to design because of its nonlinear dynamics and parameter uncertainties. In this paper, an adaptive controller is considered under the assumption that the friction coefficient is unknown. A modified high-order sliding-mode controller is designed to enhance the controller performance. The controller ensures tracking of the desired reference and identifies the unknown parameter, despite parametric variations acting on the real system. The stability proof is done using the Lyapunov approach. Some numerical and experimental tests evaluate the controller on a mechatronic system that represents a quarter-car model

Integrated braking control for electric vehicles with in-wheel propulsion and fully decoupled brake-by-wire system

This paper introduces a case study on the potential of new mechatronic chassis systems for battery electric vehicles, in this case a brake-by-wire (BBW) system and in-wheel propulsion on the rear axle combined with an integrated chassis control providing common safety features like anti-lock braking system (ABS), and enhanced functionalities, like torque blending. The presented controller was intended to also show the potential of continuous control strategies with regard to active safety, vehicle stability and driving comfort. Therefore, an integral sliding mode (ISM) and proportional integral (PI) control were used for wheel slip control (WSC) and benchmarked against each other and against classical used rule-based approach. The controller was realized in MatLab/Simulink and tested under real-time conditions in IPG CarMaker simulation environment for experimentally validated models of the target vehicle and its systems. The controller also contains robust observers for estimation of non-measurable vehicle states and parameters e.g., vehicle mass or road grade, which can have a significant influence on control performance and vehicle safety

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

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