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This paper proposes a tire traction force control scheme which effectively deals with the highly nonlinear and uncertain tire-road interaction, and variations in road conditions. Moreover, the proposed control scheme is based on robust estimations of traction forces; therefore, unlike the slip-ratio-based methods, achievement of desired traction forces is guaranteed by the proposed control scheme. Then simulations are conducted for verification. The results show that the performance of traction force estimation and control is satisfactory, even under the conditions of suddenly changed tire-road friction-coefficients and mismatched tire models for controller design and simulations. [ABSTRACT FROM PUBLISHER]

In this paper, the Dirichlet process mixture model is used to describe the distribution of the whole laser measurements in a given scan. Then the number of clusters is inferred from the measurements by the Gibbs sampler. We focus on the automotive application which usually has a more complex environment. Due to the variant shapes and sizes of the real traffic objects, the multi-class DP-based clustering model, which is incorporated with a mixture prior distribution, is proposed to cluster the measurements more properly. The clustering results of the proposed method are compared with those of several existing clustering methods both in an expressway case and in an urban road case. The corresponding tracking performances are also analyzed and the improvements of the proposed method are presented. [ABSTRACT FROM PUBLISHER]

This paper presents a motion control scheme for a front-wheel-steering/rear-wheel-driving (FWS/RWD) vehicle using the optimum tire force distribution method. The proposed control system is divided into three layers: the upper controller, the optimum tire force distribution algorithm, and the lower controller. When the driver commands the vehicle, the upper controller computes the total desired longitudinal tire force, lateral tire force, and the desired yaw moment which allow the vehicle to follow the given reference trajectory. Then the desired forces and moments are optimally distributed to the four wheels, and the lower controller compensates for the nonlinear and uncertain characteristics of the tire dynamics to generate the desired forces. Finally, simulations are carried out to verify the effectiveness of the proposed control scheme. [ABSTRACT FROM PUBLISHER]

Multiple-model (MM)-based methods have been successfully applied to many fault detection schemes; however systematic design of the associated model set remains an open question. The difficulty comes from the fact that using a large model set reduces the risk of undetected faults, but also increases the computation load drastically. In this paper we propose a dual-model fault detection (DMFD) algorithm aiming at solving the model set design problem, and apply it to detect actuator faults of robot manipulators. The DMFD algorithm is able to detect various types of unexpected actuator faults, including abrupt faults, incipient faults, and simultaneous faults, in a computationally efficient way. To evaluate the performance of the DMFD algorithm, upper bounds of the false alarm and missed detection probabilities are explicitly presented in terms of the tunable variables. Furthermore, experiments are conducted to demonstrate its ability in immediate detection of faults. [ABSTRACT FROM PUBLISHER]