Robust hovering control of a quadrotor using acceleration feedback

This paper presents a novel acceleration feedback control method for robust hovering of a quadrotor subject to aerodynamic disturbances. An acceleration based disturbance observer (ABDOB) is designed to reject disturbances acting on the positional dynamics of the quadrotor. In order to provide high stiffness against disturbances acting on the attitude dynamics, a nested position, velocity and inner acceleration feedback control structure that utilizes PID and PI type controllers is developed. To obtain reliable angular acceleration information, a cascaded estimation technique based on an extended Kalman filter (EKF) and a classical Kalman filter (KF) is proposed. EKF estimates the Euler angles and gyro biases by fusing the data from gyroscope, accelerometer and magnetometer. Compensated gyro data are then fed into a Kalman filter whose process model is derived from Taylor series expansion of angular velocities and accelerations where angular jerks are considered as stochastic inputs. The well-known kinematic relation between Euler angular rates and angular velocities is employed to estimate reliable Euler accelerations. Estimated Euler angles, rates and accelerations are then used as feedback signals in the nested attitude control structure. Performance of the proposed method is assessed by a high fidelity simulation model where uncertainties in the sensor measurements, e.g. sensor bias and noise, are also considered. Developed controllers that utilize estimated acceleration feedback provide extremely robust hovering results when the quadrotor is subject to wind gusts generated by Dryden wind model. Simulation results show that utilization of acceleration feedback in hovering control significantly reduces the deviations in the x-y position of the quadrotor

Driver evaluation in heavy duty vehicles based on acceleration and braking behaviors

In this paper, we present a real-time driver evalua-tion system for heavy-duty vehicles by focusing on the classifica-tion of risky acceleration and braking behaviors. We utilize animproved version of our previous Long Short Memory (LSTM)based acceleration behavior model [10] to evaluate varyingacceleration behaviors of a truck driver in small time periods.This model continuously classifies a driver as one of six driverclasses with specified longitudinal-lateral aggression levels, usingdriving signals as time-series inputs. The driver gets accelerationscore updates based on assigned classes and the geometry ofdriven road sections. To evaluate the braking behaviors of atruck driver, we propose a braking behavior model, which usesa novel approach to analyze deceleration patterns formed duringbrake operations. The braking score of a driver is updated foreach brake event based on the pattern, magnitude, and frequencyevaluations. The proposed driver evaluation system has achievedsignificant results in both the classification and evaluation ofacceleration and braking behaviors

Diesel engine NOx emission modeling using a new experiment design and reduced set of regressors

n this paper, NOx emissions from a diesel engine are modeled with nonlinear autoregressive with exogenous input (NARX) model. Airpath and fuelpath channels are excited by chirp signals where the frequency profile of each channel is generated by increasing the number of sweeps. Past values of the output are employed only in linear prediction with all input regressors, and the most significant input regressors are selected for the nonlinear prediction by orthogonal least square (OLS) algorithm and error reduction ratio. Experimental results show that NOx emissions can be modeled with high validation performance and models obtained using a reduced set of regressors perform better in terms of stability and robustness

Predicting NOx emissions in diesel engines via sigmoid NARX models using a new experiment design for combustion identification

Diesel engines are still widely used in heavy-duty engine industry because of their high energy conversion efficiency. In recent decades, governmental institutions limit the maximum acceptable hazardous emissions of diesel engines by stringent international regulations, which enforces engine manufacturers to find a solution for reducing the emissions while keeping the power requirements. A reliable model of the diesel engine combustion process can be quite useful to search for the best engine operating conditions. In this study, nonlinear modeling of a heavy-duty diesel engine NOx emission formation is presented. As a new experiment design, air-path and fuel-path input channels were excited by chirp signals where the frequency profile of each channel is different in terms of the number and the direction of the sweeps. This method is proposed as an alternative to the steady-state experiment design based modeling approach to substantially reduce testing time and improve modeling accuracy in transient operating conditions. Sigmoid based nonlinear autoregressive with exogenous input (NARX) model is employed to predict NOx emissions with given input set under both steady-state and transient cycles. Models for different values of parameters are generated to analyze the sensitivity to parameter changes and a parameter selection method using an easy-to-interpret map is proposed to find the best modeling parameters. Experimental results show that the steady-state and the transient validation accuracies for the majority of the obtained models are higher than 80% and 70%, respectively

OPUCEM: A Library with Error Checking Mechanism for Computing Oblique Parameters

After a brief review of the electroweak radiative corrections to gauge-boson self-energies, otherwise known as the direct and oblique corrections, a tool for calculation of the oblique parameters is presented. This tool, named OPUCEM, brings together formulas from multiple physics models and provides an error-checking machinery to improve reliability of numerical results. It also sets a novel example for an "open-formula" concept, which is an attempt to improve the reliability and reproducibility of computations in scientific publications by encouraging the authors to open-source their numerical calculation programs. Finally, we demonstrate the use of OPUCEM in two detailed case studies related to the fourth Standard Model family. The first is a generic fourth family study to find relations between the parameters compatible with the EW precision data and the second is the particular study of the Flavor Democracy predictions for both Dirac and Majorana-type neutrinos.Comment: 10 pages, 19 figures, section 3 and 4 reviewed, results unchanged, typo correction

Estimating soot emission in diesel engines using gated recurrent unit networks

In this paper, a new data-driven modeling of a diesel engine soot emission formation using gated recurrent unit (GRU) networks is proposed. Different from the traditional time series prediction methods such as nonlinear autoregressive with exogenous input (NARX) approach, GRU structure does not require the determination of the pure time delay between the inputs and the output, and the number of regressors does not have to be chosen beforehand. Gates in a GRU network enable to capture such dependencies on the past input values without any prior knowledge. As a design of experiment, 30 different points in engine speed - injected fuel quantity plane are determined and the rest of the input channels, i.e., rail pressure, main start of injection, equivalence ratio, and intake oxygen concentration are excited with chirp signals in the intended regions of operation. Experimental results show that the prediction performances of GRU based soot models are quite satisfactory with 77% training and 57% validation fit accuracies and normalized root mean square error (NRMSE) values are less than 0.038 and 0.069, respectively. GRU soot models surpass the traditional NARX based soot models in both steady-state and transient cycles