Abstract

In order to support the development process of Advanced Driver Assistance systems for road vehicles, TNO is operating a hardware-in-the-loop test setup. In this facility, called VeHIL, vehicles in the direct neighborhood of the test vehicle are simulated using wheeled mobile robots. Due to the required type of maneuvers, these robots have independently driven and steered wheels. Consequently, the robot is overactuated. Furthermore, since the robot is capable of high dynamic maneuvers, slip effects caused by the tires can play an important role. A position controller based on feedback linearization is presented, using the so-called multicycle approach which regards the robot as a set of independent unicycles. As a result, the wheeled mobile robot is position controlled while each unicycle is controlled taking weight transfer as well as longitudinal and lateral tire slip into account. © 2008 IEEE.

Abstract

Date of Conference: 11–13 October 2017 Conference Name: ASME 2017 Dynamic Systems and Control Conference, DSCC 2017 Model reference adaptive control (MRAC) can effectively handle various challenges of the real world control problems including exogenous disturbances, system uncertainties, and degraded modes of operations. In human-in-the-loop settings, MRAC may cause unstable system trajectories. Basing on our recent work on the stability of MRAC-human dynamics, here we follow an optimization based computations to design a linear filter and study whether or not this filter inserted between the human model and MRAC could help remove such instabilities, and potentially improve performance. To this end, we present a mathematical approach to study how the error dynamics of MRAC could favorably or detrimentally influence human operator's error dynamics in performing a certain task. An illustrative numerical example concludes the study.

Abstract

Date of Conference: 11–13 October 2017 Conference Name: ASME 2017 Dynamic Systems and Control Conference, DSCC 2017 The paper presents an approach to the Model Reference Adaptive Control (MRAC) design for nonlinear dynamical systems. A nonlinear reference system is considered such that its response is designed to be stable via Successive Approximation Approach (SAA). Having designed the stable reference model through the SAA, MRAC is then formulated for nonlinear plant dynamics with a new adaptation rule to guarantee the convergence of the nonlinear plant response to that of the response of the nonlinear reference model. The proposed design methodology is illustrated with examples for different case studies.

Abstract

The reduction of CO2 emissions and fuel consumption from road transportation constitutes an important pillar of the EU commitment for implementing the Kyoto Protocol. Efforts to monitor and limit CO2 emissions from vehicles can effectively be supported by the use of vehicle modelling tools. This paper presents the application of such a tool for predicting CO2 emissions of vehicles under different operating conditions and shows how the results from simulations can be used for supporting policy analysis and design aiming at further reductions of the CO2 emissions. For this purpose, the case of light duty goods (N1 category) vehicle CO2 emissions control measures adopted by the EU is analysed. In order to understand how certain design and operating aspects affect fuel consumption, a number of N1 vehicles were simulated with ADVISOR for various operating conditions and the numerical results were validated against chassis dynamometer tests. The model was then employed for analysing and evaluating the new EU legislative framework that addresses CO2 emissions from this vehicle class. The results of this analysis have shown the weaknesses of the current regulations and revealed new potential in CO2 emissions control. Finally the TREMOVE model was used for simulating a possible scenario for reducing CO2 emissions at fleet level. © 2006 Elsevier Ltd. All rights reserved. Chemicals / CAS: carbon dioxide, 124-38-9, 58561-67-4