Model-based and Koopman-based predictive control:a braking control systems comparison

Anti-locking Braking systems are crucial safety systems in modern vehicles. In this work, we investigate the possibility to use Model Predictive Control (MPC) for braking systems by considering three different models identified from data. Specifically, we consider two models, whose structure and the identification procedure are driven by physics principles, and a third black-box modeling approach that relies on Koopman theory. By comparing the effectiveness of the three resulting MPC schemes in a high-fidelity simulation environment, we show that Koopman-based MPC can generally be a viable solution for the design of braking controllers, which might not be the case of nonlinear MPC or approximated scheme like the second one we test.</p

On-line learning applied to spiking neural network for antilock braking systems

Computationally replicating the behaviour of the cerebral cortex to perform the control tasks of daily life in a human being is a challenge today. First, … Finally, a suitable learning model that allows adapting neural network response to changing conditions in the environment is also required. Spiking Neural Networks (SNN) are currently the closest approximation to biological neural networks. SNNs make use of temporal spike trains to deal with inputs and outputs, thus allowing a faster and more complex computation. In this paper, a controller based on an SNN is proposed to perform the control of an anti-lock braking system (ABS) in vehicles. To this end, two neural networks are used to regulate the braking force. The first one is devoted to estimating the optimal slip while the second one is in charge of setting the optimal braking pressure. The latter resembles biological reflex arcs to ensure stability during operation. This neural structure is used to control the fast regulation cycles that occur during ABS operation. Furthermore, an algorithm has been developed to train the network while driving. On-line learning is proposed to update the response of the controller. Hence, to cope with real conditions, a control algorithm based on neural networks that learn by making use of neural plasticity, similar to what occurs in biological systems, has been implemented. Neural connections are modulated using Spike-Timing-Dependent Plasticity (STDP) by means of a supervised learning structure using the slip error as input. Road-type detection has been included in the same neural structure. To validate and to evaluate the performance of the proposed algorithm, simulations as well as experiments in a real vehicle were carried out. The algorithm proved to be able to adapt to changes in adhesion conditions rapidly. This way, the capability of spiking neural networks to perform the full control logic of the ABS has been verified.Funding for open access charge: Universidad de Málaga / CBUA This work was partly supported by the Ministry of Science and Innovation under grant PID2019-105572RB-I00, partly by the Regional Government of Andalusia under grant UMA18-FEDERJA-109, and partly by the University of Malaga as well as the KTH Royal Institute of Technology and its initiative, TRENoP

Data-Driven Modeling and Regulation of Aircraft Brakes Degradation via Antiskid Controllers

In ground vehicles, braking actuator degradation and tire consumption do not represent a significant maintenance cost as the lifespan of both components, at least in common situations, is rather long. In the aeronautical context, and for aircraft in particular, instead, braking actuator degradation and tire consumption significantly contribute to an aircraft maintenance cost due to the frequency of their replacement. This is mainly due to the fact that aircraft braking maneuvers last significantly longer than those in the automotive context. So that the antilock braking system is always active during the braking maneuver, making its impact on the consumption of the two components significant. This work proposes an innovative data-driven model of brake and tire degradation, showing how they are related to the antiskid controller parameters. The analysis is carried out in a MATLAB/Simulink environment on a single wheel rigid body model, validated experimentally, which includes all the nonlinear effects peculiar of the aeronautic context. The results show that by using an appropriate antiskid control approach, it is possible to directly regulate the consumption of these components while at the same time guaranteeing the required braking performance

Modelling and control of the braking system of the electric Polaris Ranger all-terrain-vehicle

I mezzi ATV sono impiegati in attività forestali, di sorveglianza e soccorso. Si è vista recentemente la nascita di ATV elettrici, sinonimo di pulizia e risparmio. La possibilità di rendere questi veicoli completamente autonomi ha stimolato l'interesse del settore automotive. L' ABS in particolare, che finora è diffusa solo tra i veicoli stradali è stata introdotta e studiata. Modelli matematici per la simulazione dell'impianto frenante sono stati derivati, come base per il futuroope

Dynamic Control Applied to a Laboratory Antilock Braking System

The control of an antilock braking system is a difficult problem due to the existence of nonlinear dynamics and uncertainties of its characteristics. To overcome these issues, in this work, a dynamic nonlinear controller is proposed, based on a nonlinear observer. To evaluate its performance, this controller has been implemented on an ABS Laboratory setup, representing a quarter car model. The nonlinear observer reconstructs some of the state variables of the setup, assumed not measurable, to establish a fair benchmark for an ABS system of a real automobile. The dynamic controller ensures exponential convergence of the state estimation, as well as robustness with respect to parameter variations

Design and implementation of a real-time autonomous navigation system applied to lego robots

Teaching theoretical concepts of a real-time autonomous robot system may be a challenging task without real hardware support. The paper discusses the application of the Lego Robot for teaching multi interdisciplinary subjects to Mechatronics students. A real-time mobile robot system with perception using sensors, path planning algorithm, PID controller is used as the case to demonstrate the teaching methodology. The novelties are introduced compared to classical robotic classes: (i) the adoption of a project-based learning approach as teaching methodology; (ii) an effective real-time autonomous navigation approach for the mobile robot. However, the extendibility and applicability of the presented approach are not limited to only the educational purpose

Predictive Braking With Brake Light Detection-Field Test

Driver assistance systems, such as adaptive cruise control, are an increasing commodity in modern vehicles. Our earlier experience of radar-based adaptive cruise control has indicated repeatable abrupt behavior when approaching a stopped vehicle at high speed, which is typical for extra-urban roads. Abrupt behavior in assisted driving not only decreases the passenger trust but also reduces the comfort levels of such systems. We present a design and proof-of-concept of a machine vision-enhanced adaptive cruise controller. A machine vision-based brake light detection system was implemented and tested in order to smoothen the transition from coasting to braking and ensure speed reduction early enough. The machine vision system detects the brake lights in front, then transmits a command to the cruise controller to reduce speed. The current paper reports the speed control system design and experiments carried out to validate the system. The experiments showed the system works as designed by reducing abrupt behavior. Measurements show that brake light-assisted cruise control was able to start deceleration about three seconds earlier than a cruise controller without brake light detection. Measurements also showed increased ride comfort with the maximum deceleration and minimum jerk levels improving from 5% to 31%.Peer reviewe