732 research outputs found

    Bayesian Sensitivity Analysis of Flight Parameters in a Hard-Landing Analysis Process

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    A flight parameter sensor simulation model was developed to assess the conservatism of the landing gear component loads calculated using a typical hard-landing analysis process. Conservatism exists due to factors of safety that are incorporated into any hard-landing analysis process to account for uncertainty in the measurement of certain flight parameters. The flight parameter sensor simulation model consists of 1) an aircraft and landing gear dynamic model to determine the “actual” landing gear loads during a hard landing; 2) an aircraft sensor and data acquisition model to represent the aircraft sensors and flight data recorder systems to investigate the effect of signal processing on the flight parameters; and 3) an automated hard-landing analysis process, representative of that used by airframe and equipment manufacturers, to determine the “simulated” landing gear loads. Using a technique of Bayesian sensitivity analysis, a number of flight parameters are varied in the flight parameter sensor simulation model to gain an understanding of the sensitivity of the difference between actual and simulated loads to the individual flight parameters in symmetric and asymmetric two-point landings. This study shows that the error can be reduced by learning the true value of the following flight parameters: longitudinal tire–runway friction coefficient, aircraft vertical acceleration (related to vertical descent velocity), lateral acceleration (related to lateral velocity), Euler roll angle, mass, center of gravity position, and main landing gear tire type. It was also shown that, due to the modeling techniques used, shock absorber servicing state and tire pressure do not contribute significantly to the error

    Tire-Road Friction Estimation Using Slip-based Observers

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    In order to improve the security of the vehicles, the car industry focuses more and more on Active Safety. The objective is to introduce embedded electronic control systems to detect dangerous conditions, warn the driver and, in emergency situations, even take actions to avoid crash or at least reduce the violence of the impact. The tire-road friction coefficient which defines the maximum traction and braking capacities is very useful information for both the driver and electronic devices like ABS, ESP, roll-over prevention or collision mitigation. Unfortunately such a coefficient cannot be directly measured and has to be estimated from other available data. This thesis reviews the main directions followed by researchers around the world and then focuses on slip-based methods. The methods proposed in a few papers are implemented, compared and commented. Then some original solutions are proposed. First, a hybrid observer has been developed with the idea to classify roads into a few categories. The principle is very interesting and the implementation brings out many problems to take into consideration and some attempts of solutions.Secondly, a force observer and a tire friction model are combined. This natural approach works well in simulations

    Driver behavior classification and lateral control for automobile safety systems

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    Advanced driver assistance systems (ADAS) have been developed to help drivers maintain stability, improve road safety, and avoid potential collision. The data acquisition equipment that can be used to measure the state and parameter information of the vehicle may not be available for a standard passenger car due to economical and technical limitations. This work focuses on developing three technologies (longitudinal tire force estimation, driver behavior classification and lateral control) using low-cost sensors that can be utilized in ADAS. For the longitudinal tire force estimation, a low cost 1Hz positioning global system (GPS) and a steering angle sensor are used as the vehicle data acquisition equipment. A nonlinear extended two-wheel vehicle dynamic model is employed. The sideslip angle and the yaw rate are estimated by discrete Kalman Filter. A time independent piecewise optimization scheme is proposed to provide time-continuous estimates of longitude tire force, which can be transferred to the throttle/brake pedal position. The proposed method can be validated by the estimation results. Driver behavior classification systems can detect unsafe driver behavior and avoid potentially dangerous situations. To realize this strategy, a machine learning classification method, Gaussian Mixture model (GMM), is applied to classify driver behavior. In this application, a low cost 1Hz GPS receiver is considered as the vehicle data acquisition equipment instead of other more costly sensors (such as steering angle sensor, throttle/brake position sensor, and etc.). Since the driving information is limited, the nonlinear extended two-wheel vehicle dynamic model is adopted to reconstruct the driver behavior. Firstly, the sideslip angle and the yaw rate are calculated since they are not available from the GPS measurements. Secondly, a piecewise optimization scheme is proposed to reproduce the steering angle and the longitudinal force. Finally, a GMM classifier is trained to identify abnormal driver behavior. The simulation results demonstrated that the proposed scenario can detect the unsafe driver behavior effectively. The lateral control system developed in this study is a look-down reference system which uses a magnetic sensor at the front bumper to measure the front lateral displacement and a GPS to measure the vehicle\u27s heading orientation. Firstly, the steering angles can be estimated by using the data provided by the front magnetic sensor and GPS. The estimation algorithm is an observer for a new extended single-track model, in which the steering angle and its derivative are viewed as two state variables. Secondly, the road curvature is determined based on the linear relationship with respect to the steering angle. Thirdly, an accurate and real-time estimation of the vehicle\u27s lateral displacements can be accomplished according to a state observer. Finally, the closed loop controller is used as a compensator for automated steering. The proposed estimation and control algorithms are validated by simulation results. The results showed that this lateral steering control system achieved a good and robust performance for vehicles following or tracking a reference path

    Holistic Vehicle Control Using Learning MPC

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    In recent years, learning MPC schemes have been introduced to address these challenges of traditional MPC. They typically leverage different machine learning techniques to learn the system dynamics directly from data, allowing it to handle model uncertainty more effectively. Besides, they can adapt to changes by continuously updating the learned model using real-time data, ensuring that the controller remains effective even as the system evolves. However, there are some challenges for the existing learning MPC techniques. Firstly, learning-based control approaches often lack interpretability. Understanding and interpreting the learned models and their learning and prediction processes are crucial for safety critical systems such as vehicle stability systems. Secondly, existing learning MPC techniques rely solely on learned models, which might result in poor performance or instability if the model encounters scenarios that differ significantly from the training data. Thirdly, existing learning MPC techniques typically require large amounts of high-quality data for training accurate models, which can be expensive or impractical in the vehicle stability control domain. To address these challenges, this thesis proposes a novel hybrid learning MPC approach for HVC. The main objective is to leverage the capabilities of machine learning algorithms to learn accurate and adaptive models of vehicle dynamics from data, enabling enhanced control strategies for improved stability and maneuverability. The hybrid learning MPC scheme maintains a traditional physics-based vehicle model and a data-based learning model. In the learned model, a variety of machine-learning techniques can be used to predict vehicle dynamics based on learning from collected vehicle data. The performance of the developed hybrid learning MPC controller using torque vectoring (TV) as the actuator is evaluated through the Matlab/Simulink and CarSim co-simulation with a high-fidelity Chevy Equinox vehicle model under a series of harsh maneuvers. Extensive real-world experiments using a Chevy Equinox electric testing vehicle are conducted. Both simulation results and experimental results show that the developed hybrid learning MPC approach consistently outperforms existing MPC methods with better yaw rate tracking performance and smaller vehicle sideslip under various driving conditions

    Empowerment for Continuous Agent-Environment Systems

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    This paper develops generalizations of empowerment to continuous states. Empowerment is a recently introduced information-theoretic quantity motivated by hypotheses about the efficiency of the sensorimotor loop in biological organisms, but also from considerations stemming from curiosity-driven learning. Empowemerment measures, for agent-environment systems with stochastic transitions, how much influence an agent has on its environment, but only that influence that can be sensed by the agent sensors. It is an information-theoretic generalization of joint controllability (influence on environment) and observability (measurement by sensors) of the environment by the agent, both controllability and observability being usually defined in control theory as the dimensionality of the control/observation spaces. Earlier work has shown that empowerment has various interesting and relevant properties, e.g., it allows us to identify salient states using only the dynamics, and it can act as intrinsic reward without requiring an external reward. However, in this previous work empowerment was limited to the case of small-scale and discrete domains and furthermore state transition probabilities were assumed to be known. The goal of this paper is to extend empowerment to the significantly more important and relevant case of continuous vector-valued state spaces and initially unknown state transition probabilities. The continuous state space is addressed by Monte-Carlo approximation; the unknown transitions are addressed by model learning and prediction for which we apply Gaussian processes regression with iterated forecasting. In a number of well-known continuous control tasks we examine the dynamics induced by empowerment and include an application to exploration and online model learning
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