scholarly journals Modeling and Deployment of Model-Based Decentralized Embedded Diagnosis inside Vehicles: Application to Smart Distance Keeping Function

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Othman Nasri ◽  
Hassan Shraim ◽  
Phillippe Dague ◽  
Olivier Heron ◽  
Michael Cartron
Keyword(s):  
Adaptive Cruise Control ◽  
Can Bus ◽  
Diagnostic Information ◽  
Hardware In The Loop ◽  
Cruise Control ◽  
Safe Distance ◽  
Model Based ◽  
Control Units ◽  
On Line ◽  
Decentralized Architecture

The deployment of a fault diagnosis strategy in the Smart Distance Keeping (SDK) system with a decentralized architecture is presented. The SDK system is an advanced Adaptive Cruise Control (ACC) system implemented in a Renault-Volvo Trucks vehicle to increase safety by overcoming some ACC limitations. One of the main differences between this new system and the classical ACC is the choice of the safe distance. This latter is the distance between the vehicle equipped with the ACC or the SDK system and the obstacle-in-front (which may be another vehicle). It is supposed fixed in the case of the ACC, while variable in the case of the SDK. The variation of this distance depends essentially on the relative velocity between the vehicle and the obstacle-in-front. The main goal of this work is to analyze measurements, issued from the SDK elements, in order to detect, to localize, and to identify some faults that may occur. Our main contribution is the proposition of a decentralized approach permitting to carry out an on-line diagnosis without computing the global model and to achieve most of the work locally avoiding huge extra diagnostic information traffic between components. After a detailed description of the SDK system, this paper explains the model-based decentralized solution and its application to the embedded diagnosis of the SDK system inside Renault-Volvo Truck with five control units connected via a CAN-bus using “Hardware in the Loop” (HIL) technique. We also discuss the constraints that must be fulfilled.

Distributed Network System for Real-Time Model Based Control of Industrial Gas Turbines

2011 ◽  
Author(s):  
V. Panov
Keyword(s):  
Control System ◽  
Real Time ◽  
Gas Turbine ◽  
Distributed Network ◽  
Hardware In The Loop ◽  
Time Model ◽  
Model Based Control ◽  
Model Based ◽  
On Line ◽  
Dedicated Hardware

This paper describes the development of a distributed network system for real-time model based control of industrial gas turbine engines. Distributed control systems contribute toward improvements in performance, testability, control system maintainability and overall life-cycle cost. The goal of this programme was to offer a modular platform for improved model based control system. Hence, another important aspect of this programme was real-time implementation of non-linear aero-thermal gas turbine models on a dedicated hardware platform. Two typical applications of real-time engine models, namely hardware-in-the-loop simulations and on-line co-simulations, have been considered in this programme. Hardware-in-the-loop platform has been proposed as a transitional architecture, which should lead towards a fully distributed on-line model based control system. Distributed control system architecture offers the possibility of integrating a real-time on-line engine model embedded within a dedicated hardware platform. Real-time executing models use engine operating conditions to generate expected values for measured and non-measured engine parameters. These virtual measurements can be used for the development of model based control methods, which can contribute towards improvements in engine stability, performance and life management. As an illustration of model based control concept, the example of gas turbine transient over-temperature protection is presented in this study.

scholarly journals Adaptive Cruise Control Strategy Design with Optimized Active Braking Control Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Wenguang Wu ◽  
Debiao Zou ◽  
Jian Ou ◽  
Lin Hu
Keyword(s):  
Predictive Control ◽  
Control Algorithm ◽  
Adaptive Cruise Control ◽  
Ride Comfort ◽  
Hierarchical Control ◽  
Cruise Control ◽  
Safe Distance ◽  
Model Predictive Control Algorithm ◽  
Braking Control ◽  
Strategy Design

The braking quality is considered as the most important performance of the adaptive control system that influences the vehicle safety and ride comfort remarkably. This research is aimed at designing an adaptive cruise control (ACC) system based on active braking algorithm using hierarchical control. Taking into account the vehicle with safety and comfort, the upper decision-making controller is designed based on model predictive control algorithm. Throttle controller and braking controller are designed with feedforward and feedback algorithms as the bottom controller, where the braking controller is designed based on the hydraulic braking model. The whole model is simulated collaboratively with Amesim, Carsim, and Simulink. By comparison with the full deceleration model, the results show that the proposed algorithm can not only make the vehicle maintain a safe distance under the premise of following the target vehicle ahead effectively but also provide favorable driving comfort.

scholarly journals Model-Based String Stability of Adaptive Cruise Control Systems Using Field Data

2020 ◽  
Vol 5 (1) ◽  
pp. 90-99 ◽  
Author(s):  
George Gunter ◽  
Caroline Janssen ◽  
William Barbour ◽  
Raphael E. Stern ◽  
Daniel B. Work
Keyword(s):  
Control Systems ◽  
Field Data ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
String Stability ◽  
Model Based

scholarly journals Model-based Predictive Control implementation for Cooperative Adaptive Cruise Control

2018 ◽  
Vol 2 (1) ◽  
pp. 1-10
Author(s):  
António Lopes ◽  
Rui Esteves Araújo
Keyword(s):  
Adaptive Cruise Control ◽  
Longitudinal Velocity ◽  
System Stability ◽  
Control Input ◽  
Vehicular Communication ◽  
Cruise Control ◽  
Model Based ◽  
Cooperative Adaptive Cruise Control ◽  
Vehicle Platoon ◽  
Predictive Controller

The automation of road vehicles has become a necessity to improve the efficiency and safety of this system. In a vehicle formation it is important to maintain a safety distance between the vehicles. The control of a vehicle spacing distance and longitudinal velocity can be achieved through the implementation of a model-based predictive controller. This implementation of a cooperative adaptive cruise control allows the access of another vehicle state information through vehicular communication technology and promote state prediction and ultimately system stability. The optimization algorithm performs the computation of the control input in a control horizon window and ensures that the spacing error takes only positive values. The results of the proposed controller are evaluated through the computational tool Simulink in the two-vehicle platoon. The controller is implemented in the precedent vehicle. To assess the performance of the proposed controller different control parameters and constraints were used.

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