This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a “pseudo-roll angle” through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors’ estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator.
Nowadays, the current vehicles are incorporating control systems in order to improve their stability and handling. These control systems need to know the vehicle dynamics through the variables (lateral acceleration, roll rate, roll angle, sideslip angle, etc.) that are obtained or estimated from sensors. For this goal, it is necessary to mount on vehicles not only low-cost sensors, but also low-cost embedded systems, which allow acquiring data from sensors and executing the developed algorithms to estimate and to control with novel higher speed computing. All these devices have to be integrated in an adequate architecture with enough performance in terms of accuracy, reliability and processing time. In this article, an architecture to carry out the estimation and control of vehicle dynamics has been developed. This architecture was designed considering the basic principles of IoT and integrates low-cost sensors and embedded hardware for orchestrating the experiments. A comparison of two different low-cost systems in terms of accuracy, acquisition time and reliability has been done. Both devices have been compared with the VBOX device from Racelogic, which has been used as the ground truth. The comparison has been made from tests carried out in a real vehicle. The lateral acceleration and roll rate have been analyzed in order to quantify the error of these devices.
Resistance strain gauges have been used for the measurement of strain for more than 50 years; however, research to quantify the inherent uncertainty in a strain-measuring system has been scarce hitherto. Nevertheless, resistive strain gauges are the most widely used tool to measure strain owing to their simplicity, apparent accuracy, low cost, and ease of use. In spite of this, at times they are used improperly, and the sources of error are neglected. Every type of measurement has an uncertainty associated with it. As it is impossible to eliminate error completely, the goal must be to quantify it and to reduce it to a value that is acceptable for the purposes of the measurement being taken. The novelty of the present research is to put forward a new methodology for determining the uncertainty in a strain gauge measuring system. To achieve this, the principal sources of error that influence the measuring system are formulated in order to develop an error model. Subsequently, the law of propagation of uncertainty is applied, together with a type A and B evaluation approach to determine the combined uncertainty of the entire measuring system, taking into consideration the correlation between variables, when applicable. The new methodology is then applied to a series of strain measurements taken on an aluminium flat bar subject to a bending load, and the results are discussed.
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