Search results

Reducing fuel consumption of unmanned aerial vehicles (UAVs) during transient operation is a cornerstone to achieve environment-friendly operations. The purpose of this paper is to develop a control scheme that improves the fuel economy of a turbojet in its full operating envelope.

A novel direct-thrust linear quadratic integral (LQI) approach, comprised by an optimal observer/controller satisfying specified performance parameters, is presented. The thrust estimator, based in a Wiener model, is validated with the experimental data of a micro-turbojet. Model uncertainty is characterized by analyzing variations between the identified model and measured data. The resulting uncertainty range is used to verify closed-loop stability with the circle criterion. The proposed controller provides stable responses with the specified performance in the whole operating range, even with after considering plant nonlinearities. Finally, the direct-thrust LQI is compared with a standard thrust controller to assess fuel economy and performance.

The direct-thrust LQI approach reduced the fuel consumption by 2.1090% in the most realistic scenario. The controllers were also evaluated using the environmental effect parameter (EEP) and transient-thrust-specific fuel consumption (T-TSFC). These novel metrics are proposed to evaluate the environmental impact during transient-thrust operations. The direct-thrust LQI approach has a more efficient fuel consumption according to these metrics. The results also show that isolating the thrust dynamics within the feedback loop has an important impact in fuel economy. Controllers were also evaluated using the EEP and T-TSFC. These novel metrics are proposed to evaluate the environmental impact during transient-thrust operations. The direct-thrust LQI approach has a more efficient fuel consumption according to these metrics. The results also show that isolating the thrust dynamics within the feedback loop has an important impact in fuel economy.

This study shows the design of an effective direct-thrust control approach that minimizes fuel consumption, ensures stable responses for the full operation range, allows isolating the thrust dynamics when designing the controller and is compatible with classical robustness and performance metrics. Finally, the study shows that a simple controller can reduce the fuel consumption of the turbojet during transient operation in scenarios that approximate realistic operating conditions.

The purpose of this study is to build a high accuracy thrust model under various small turbojet engine shaft speeds by using robust, ordinary, linear and nonlinear least squares estimation methods for target drone applications.

The dynamic shaft speeds from the test experiment of a target drone engine is conducted. Then, thrust values are calculated. Based on these, the engine thrust is modeled by robust linear and nonlinear equations. The models are benefited from quadratic, power and various series expansion functions with several coefficients to optimize the model parameters.

The error terms and accuracy of the model are given using sum of squared errors, root mean square error (RMSE) and R-squared (R²) error definitions. Based on the multiple analyses, it is seen that the RMSE values are no more than 17.7539 and the best obtained result for robust least squares estimation is 15.0086 for linear at all cases. Furthermore, the R² value is found to be 0.9996 as the highest with the nonlinear Fourier series expansion model.

The motivation behind this paper is to propose robust nonlinear thrust models based on power, Fourier and various series expansion functions for dynamic shaft speeds from the test experiments.

The purpose of this paper is to analyse the accuracy of the thrust force of a linear actuator computed with different finite elements models.

A series of 2D and 3D models corresponding to different levels of approximation of the original problem are considered. A reliable error estimator based on dual magnetostatic formulations is used.

A 3D model does not necessarily ensure more accurate results than a 2D model. Because of limitations on the number of mesh elements, the discretisation error in 3D can be of the same order of magnitude as the error introduced by the 2D approximation.

The results emphasise the need to consider errors arising from different simplifications with respect to one another, in order to avoid improvements of the model increasing the complexity but not improving the accuracy of the results.

Hydrodynamic parameter estimation is significant for the velocity prediction of unmanned surface vehicles. Considering the field data’s uncertain nonlinearities (environmental disturbances and measurement noise), this paper aims to propose a hybrid adaptive parameter estimation (HAPE) strategy.

First, a rough estimation of hydrodynamic parameters is used by the least squares method. Second, an improved adaptive parameter estimation algorithm is applied to compensate for the influence of uncertain nonlinearities and adjust the parameters within the rough range. Finally, it is proved that the calculated velocity asymptotically converges to the actual value during the parameter estimation procedure.

The numerical simulation and pool experiments are conducted in two scenarios of steady turning and sinusoidal thrust to verify the effectiveness of the proposed HAPE method. The results validate that the accuracy of the predicted velocity using the hydrodynamic model obtained by the HAPE strategy is better than the APE algorithm. In addition, the hydrodynamic parameters estimated with the sinusoidal thrust data are more applicable than the steady turning data.

This study proposes a HAPE strategy that considers the uncertain nonlinearities of the field data. This method provides a more accurate predicted velocity. Besides, as far as we know, it is the first time to analyze the influence of different test conditions on the accuracy of the predicted velocity.

The end-effects is a well-recognized phenomenon occurring in the linear induction motor (LIM) which makes the analysis and control of the LIM with good performance very difficult and can cause additional significant non-linearities in the model. So, the compensation of parameters uncertainties due to these effects in the control system is very necessary to get a robust speed control. The purpose of this paper is to propose a new technique of LIM end-effects estimation using the inverse rotor time constant tuning in order to compensate the flux orientation error in the indirect field-oriented control (IFOC) control law.

First, the dynamic model of the LIM taking into consideration the end-effects based on Duncan model is derived. Then, the IFOC for LIM speed control with end-effects compensation is derived. Finally, a new technique of LIM end-effects estimation is proposed based on the model reference adaptive system (MRAS) theory using the instantaneous active power and the estimated stator currents vector. These estimated currents are obtained through the solution of LIM state equations.

Simulations were carried out in MATLAB/SIMULINK to demonstrate the effectiveness and robustness of LIM speed control with the proposed MRAS inverse rotor time constant tuning to estimate end-effects value. The numerical validation results show that the proposed scheme permits the drive to achieve good dynamic performance, satisfactory for the estimated end-effects of the LIM model and robustness to uncertainties.

The end-effects causes a drop in the magnetizing, primary and the secondary inductance, requiring a more complex LIM control scheme. This paper presents a new approach of LIM end-effect estimation based on the online adaptation and tuning of the LIM inductances. The proposed scheme use the inverse rotor time constant tuning for end-effects correction in LIM vector control block.

The purpose of this paper is to present an improved particle filter-based attitude estimator for a quadrotor unmanned aerial vehicle (UAV) that addresses the degeneracy issues.

Control of a quadrotor is not sufficient enough without an estimator to eliminate the noise from low-cost sensors. In this work, particle filter-based attitude estimator is proposed and used for nonlinear quadrotor dynamics. But, since recursive Bayesian estimation steps may rise degeneracy issues, the proposed scheme is improved with four different and widely used resampling algorithms.

Robustness of the proposed schemes is tested under various scenarios that include different levels of uncertainty and different particle sizes. Statistical analyses are conducted to assess the error performance of the schemes. According to the statistical analysis, the proposed estimators are capable of reducing sensor noise up to 5x, increasing signal to noise ratio up to 2.5x and reducing the uncertainty bounds up to 36x with root mean square value of as low as 0.0024, mean absolute error value of 0.036, respectively.

To the best of the authors’ knowledge, the originality of this paper is to propose a robust particle filter-based attitude estimator to eliminate the low-cost sensor errors of quadrotor UAVs.

Discusses the 27 papers in ISEF 1999 Proceedings on the subject of electromagnetisms. States the groups of papers cover such subjects within the discipline as: induction machines; reluctance motors; PM motors; transformers and reactors; and special problems and applications. Debates all of these in great detail and itemizes each with greater in‐depth discussion of the various technical applications and areas. Concludes that the recommendations made should be adhered to.

The purpose of this paper is to introduce a motion control method for WFF-AmphiRobot, which can effectively realize the flexible motion of the robot on land, underwater and in the transition zone between land and water.

Based on the dynamics model, the authors selected the appropriate state variables to construct the state space model of the robot and estimated the feedback state of the robot through the maximum a posteriori probability estimation. The nonlinear predictive model controller of the robot is constructed by local linearization of the model to perform closed-loop control on the overall motion of the robot. For the control problem of the terminal trajectory, using the neural rhythmic movement theory in bionics to construct a robot central pattern generator (CPG) for real-time generation of terminal trajectory.

In this paper, the motion state of WFF-AmphiRobot is estimated, and a model-based overall motion controller for the robot and an end-effector controller based on neural rhythm control are constructed. The effectiveness of the controller and motion control algorithm is verified by simulation and physical prototype motion experiments on land and underwater, and the robot can ideally complete the desired behavior.

The paper designed a controller for WFF-AmphiRobot. First, when constructing the robot state estimator in this paper, the robot dynamics model is introduced as the a priori estimation model, and the error compensation of the a priori model is performed by the method of maximum a posteriori probability estimation, which improves the accuracy of the state estimator. Second, for the underwater oscillation motion characteristics of the flipper, the Hopf oscillator is used as the basis, and the flipper fluctuation equation is modified and improved by the CPG signal is adapted to the flipper oscillation demand. The controller effectively controls the position error and heading angle error within the desired range during the movement of the WFF-AmphiRobot.

It is of a great significance for the health monitoring of a liquid rocket engine to build an accurate and reliable fault prediction model. The thrust of a liquid rocket engine is an important indicator for its health monitoring. By predicting the changing value of the thrust, it can be judged whether the engine will fail at a certain time. However, the thrust is affected by various factors, and it is difficult to establish an accurate mathematical model. Thus, this study uses a mixture non-parametric regression prediction model to establish the model of the thrust for the health monitoring of a liquid rocket engine.

This study analyzes the characteristics of the least squares support vector regression (LS-SVR) machine . LS-SVR is suitable to model on the small samples and high dimensional data, but the performance of LS-SVR is greatly affected by its key parameters. Thus, this study implements the advanced intelligent algorithm, the real double-chain coding target gradient quantum genetic algorithm (DCQGA), to optimize these parameters, and the regression prediction model LSSVRDCQGA is proposed. Then the proposed model is used to model the thrust of a liquid rocket engine.

The simulation results show that: the average relative error (ARE) on the test samples is 0.37% when using LS-SVR, but it is 0.3186% when using LSSVRDCQGA on the same samples.

The proposed model of LSSVRDCQGA in this study is effective to the fault prediction on the small sample and multidimensional data, and has a certain promotion.

The original contribution of this study is to establish a mixture non-parametric regression prediction model of LSSVRDCQGA and properly resolve the problem of the health monitoring of a liquid rocket engine along with modeling the thrust of the engine by using LSSVRDCQGA.

THE experimental determination of the moment of inertia of a body is frequently required to confirm a calculated value or to eliminate the tedious work involved in the calculation. This is normally done by integrating the body into a vibrating system, such as a pendulum.