Hybrid Kinematic-Dynamic Sideslip and Friction Estimation

Vehicle sideslip and tyre/road friction are crucial variables for advanced vehicle stability control systems. Estimation is required since direct measurement through sensors is costly and unreliable. In this paper, we develop and validate a sideslip estimator robust to unknown road grip conditions. Particularly, the paper addresses the problem of rapid tyre/road friction adaptation when sudden road condition variations happen. The algorithm is based on a hybrid kinematic-dynamic closed-loop observer augmented with a tyre/road friction classifier that reinitializes the states of the estimator when a change of friction is detected. Extensive experiments on a four wheel drive electric vehicle carried out on different roads quantitatively validate the approach. The architecture guarantees accurate estimation on dry and wet asphalt and snow terrain with a maximum sideslip estimation error lower than 1.5 deg. The classifier correctly recognizes 87% of the friction changes; wrongly classifies 2% of the friction changes while it is unable to detect the change in 11% of the cases. The missed detections are due to the fact that the algorithm requires a certain level of vehicle excitation to detect a change of friction. The average classification time is 1.6 s. The tests also indicate the advantages of the friction classifiers on the sideslip estimation error

On optimal gear shifting in city bicycles

Standard and electric bicycles are expected to become the principal mean of transport for future short-range mobility. Solving the comfort problem is thus becoming more and more urgent, nonetheless it is known to be a hard task, especially because the cyclist is an active agent while pedaling. As far as we are aware, this is the first paper addressing the comfort problem during the gear shifting phase. Under non-restrictive assumptions, an algorithmic solution is developed providing an estimate for the best instant for comfort shifting. This problem is formulated as a local minimum acceleration point seeking. To solve it, the pedaling cadence is estimated from the rear wheel speed, after learning the gear ratios. Experiments performed on a real testing set-up are finally provided to assess the performance of the proposed approach. (c) 2022 Elsevier Ltd. All rights reserved

Data-driven clamping force control for an Electric Parking Brake without speed measurement

This paper addresses the control of the clamping force provided by an Electric Parking Brake (EPB). A simple on-off strategy is implemented: the device is actuated until the actual force reaches the target value maintaining the vehicle in a steady position. The effectiveness of the control is then highly depending on the quality of the clamping force estimation. The proposed estimator relies on the sole DC motor current and voltage signals and does not require the knowledge of any physical parameters nor the measurement of the DC motor angular displacement. Extensive eperimental tests show the robustness of the proposed strategy with respect to different operating and aging conditions

An application of the virtual reference feedback tuning method to a benchmark problem

Virtual Reference Feedback Tuning (VRFT) is a general methodology for the design of a controller when the plant transfer function is unknown, proposed by the same authors in previous contributions. It is a direct method that aims at minimizing a control cost of the 2-norm type by using a batch of data collected from the plant. The minimisation is conducted in one-shot (the method is not iterative) and this makes VRFT particularly handy in many practical applications. This paper presents an application of VRFT to a benchmark active suspension system. As a by-product, this paper also delivers a new extension of VRFT that permits to cope with constraints on the input-sensitivity

Tuning regularization via scenario optimization

In learning problems, avoiding to overfit the training data is of fundamental importance in order to achieve good predictive capabilities. Regularization networks have shown to be an effective tool to find reliable models, however their tuning is all but straightforward. In this paper, we consider learning problems that can be formulated as random convex minimization programs, and leverage on recent results established within the Wait & Judge theory for scenario optimization. Our main result is that, within this framework, generalization is deeply connected to the number of so-called support points found in optimization. By suitably selecting the regularization parameter, one can adjust the support points set and thereby can tune the trade-off between performance and generalization of the solution on the ground of a rigorous and quantitative theory

A Toolbox for Virtual Reference Feedback Tuning (VRFT)

The Virtual Reference Feedback Tuning (VRFT) is a direct data-driven method for controller design. In this paper, we present a MATLAB toolbox that implements the most important procedures of VRFT. The Toolbox is freely available online. The main VRFT procedures are described in this paper from a user-oriented point of view, and are illustrated on some numerical examples with a linear and a nonlinear plant. The tuning of a proportional-integral-derivative (PID) controller will serve as a running example throughout the paper

Differential braking-based anti-rollover control for non-tilting narrow-track vehicles

This paper presents an anti-rollover control system for a non-tilting narrow-track four-wheeled vehicle. The control system relies on two independent braking actuators on the rear wheels. The control system, designed to prevent rollover while limiting the reduction of speed, has two key elements: a steady state term that handles the static roll over limits and a dynamic term that intervenes during fast transients. The control system is tuned based on a control oriented model, identified from a multi-body simulator of the vehicle. The paper validates the proposed control system for steady state and dynamic maneuvers and compares the results against an acausal optimal benchmark. This analysis shows that the control system prevents rollover for all velocities and, in the worst case, reduces the velocity of an additional 25% with respect to the acausal optimal benchmark

Robust Experiment Design for Virtual Reference Feedback Tuning

This paper deals with robust experiment design for the Virtual Reference Feedback Tuning (VRFT) approach, a non-iterative control design method aimed to tune fixed-order controllers directly from experimental data, without the need for a model of the plant. In a previous contribution, it has been shown that the spectrum of the optimal input depends on the frequency response of the controller achieving the desired performance. In this work, a robust input design procedure is proposed, which requires only mild prior knowledge about the optimal controller. The solution is obtained analytically via constrained min-max optimization. Simulation results on a benchmark case study for digital control systems show the effectiveness of the proposed approach

Virtual reference feedback tuning for two degree of freedom controllers

The virtual reference feedback tuning (VRFT) is a data-based method for the design of feedback controllers. In the original formulation, the VRFT method gives a solution to the degree of freedom model-reference control problem in which the objective is to shape the input-output transfer function of the control system. In this paper, the extension of the method to the design of 2 d.o.f. controllers is presented and discussed. Copyright (C) 2002 John Wiley Sons, Ltd