Data Mining - Concepts and Techniques.

This paper recalls a few past achievements in Model Predictive Control, gives an overview of some current developments and suggests a few avenues for future research.

Model Predictive Control

Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

System Identification I

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

Braking Control Systems Design: Introduction and Modelling.- to Active Braking Control Systems.- Control-oriented Models of Braking Dynamics.- Braking Control Systems Design: Basic Solutions.- Braking Control Systems Design: Actuators with Continuous Dynamics.- Braking Control Systems Design: Actuators with Discrete Dynamics.- Longitudinal Wheel Slip Estimation.- Braking Control Systems Design: Advanced Solutions.- Mixed Slip and Deceleration Control.- Nonlinear Wheel Slip Control Design.- Identification of Tyre-road Friction Conditions.

Active Braking Control Systems Design for Vehicles

Robust nonlinear output feedback control for brake by wire control systems

In road vehicles, wheel locking can be prevented by means of closed-loop anti-lock braking systems (ABS). Automatic braking is extensively used also for electronic stability control (ESC) systems. In braking control systems, two output variables are usually considered for regulation purposes: wheel deceleration and wheel longitudinal slip. Wheel deceleration is the controlled output traditionally used in ABS, since it can be easily measured with a simple wheel encoder; however, the dynamics of a classical regulation loop on the wheel deceleration critically depend on the road conditions. A regulation loop on the wheel longitudinal slip is simpler and dynamically robust; moreover, slip control is perfectly suited for both ABS and ESC applications. However, the wheel-slip measurement is critical, since it requires the estimation of the longitudinal speed of the vehicle body, which cannot be directly measured. Noise sensitivity of slip control hence is a critical issue, especially at low speed. In this work a new control strategy called mixed slip-deceleration (MSD) control is proposed: the basic idea is that the regulated variable is a convex combination of wheel deceleration and longitudinal slip. This strategy turns out to be very powerful and flexible: it inherits all the attractive dynamical features of slip control, while providing a much lower sensitivity to slip-measurement noise.

Mixed Slip-Deceleration Control in Automotive Braking Systems

Among the many opportunities offered by electric vehicles (EVs), the design of power trains based on in-wheel electric motors represents, from the vehicle dynamics point of view, a very attractive prospect, mainly due to the torque-vectoring capabilities. However, this distributed propulsion also poses some practical challenges, owing to the constraints arising from motor installation in a confined space, to the increased unsprung mass weight and to the integration of the electric motor with the friction brakes. This last issue is the main theme of this work, which, in particular, focuses on the design of the anti-lock braking system (ABS). The proposed structure for the ABS is composed of a tyre slip controller, a wheel torque allocator and a braking supervisor. To address the slip regulation problem, an adaptive controller is devised, offering robustness to uncertainties in the tyre–road friction and featuring a gain-scheduling mechanism based on the vehicle velocity. Further, an optimisation framework ...

/pdf/torque-blending-and-wheel-slip-control-in-evs-with-in-wheel-4nr2o0v834.pdf

Torque blending and wheel slip control in EVs with in-wheel motors

Tire-road friction characteristics are deeply interlaced with all vehicle dynamics control systems, as road conditions strongly affect the control schemes behaviour. This work aims at the real-time estimation of the wheel slip value corresponding to the peak of the tire-road friction curve, in order to provide anti-lock braking systems (ABS) with reliable information on its value upon activation. Different techniques based on recursive least squares and the maximum likelihood approach are used for friction curve fitting and their merits and drawbacks thoroughly examined. In addition, since one of the main issues in slip-based friction estimation during braking is vehicle speed estimation, an effective algorithm for addressing this task is developed. The proposed peak slip value estimation strategy is analysed and tested both in simulation and on data collected on an instrumented test vehicle. In the latter case, the vehicle speed estimation algorithm is used, and the estimated vehicle speed provided as input for friction estimation. Practical applicability constraints posed by typical ABS systems are also considered.

Mara Tanelli

Papers

Active Braking Control Systems Design for Vehicles

Robust nonlinear output feedback control for brake by wire control systems

Mixed Slip-Deceleration Control in Automotive Braking Systems

Torque blending and wheel slip control in EVs with in-wheel motors

Real-time identification of tire-road friction conditions