This paper presents a robust control strategy for a Permanent Magnet Synchronous Machine (PMSM) based on passivity theory. Pre‐control terms ensure robustness to variation of parameters. The nominal electrical and mechanical dynamics are treated separately and a cascade structure is obtained. A comparative analysis is done in Matlab‐Simulink with a Simple Adaptive Control (SAC) strategy in terms of settling time, stationary error, time response and energy efficiency. Improvements of the proposed Passivity Based Control (PBC) strategy are shown in comparison with some other PBC controllers.

Permanent magnet synchronous motors (PMSM) are widely used in electric vehicle application due to their high power density, high efficiency and very good performance at low speeds. Safety concerns in automotive industry require monitoring of the system to ensure a correct and safe operation of the electric vehicle. This paper presents three software methods for estimating the winding electrical resistance and detecting an open phase failure during torque control operation of a PMSM. All three methods can be applied online. Differences between these methods are reflected into computational effort and efficiency. The described methods were numerically simulated and tested in MATLAB. The first two methods monitor the electrical resistance of the stator windings which are estimated through an Extended Kalman Filter (EKF) and an Unscented Kalman Filter (UKF). Hence, the three phase model and the rotating reference frame model are both used as internal models for the proposed Kalman filters and the estimation performance is evaluated in each case. In order to detect an open phase fault, the gradient of the estimated electrical resistance is monitored. The third method which is independent of the motor parameters calculates the Discrete Fourier Transformation (DFT) coefficients of the fundamental frequency in the phase currents signals. Hence, due to the low computational effort and good performance, the Goertzel algorithm is proposed. Conclusions are drawn based on estimation accuracy, time response and complexity of each method.

Here we report on an analytical study of the unsteady aerodynamic interactions of a closely coupled, co-rotating, high- and low-pressure turbine configuration. The effort was focused on the prediction of unsteady surface pressures imparted on the first blade of the low-pressure turbine (LPT). As a first step, a baseline three-row time-accurate prediction was carried out for the first three rows of the low-pressure turbine (vane-blade-vane). In contrast to the three-row results, a four-row analysis, which included the blade of the high-pressure turbine, revealed that the temporally varying tangential load on the LPT blade was increased in amplitude by a factor of five compared to the three-row case with a shift in primary unsteady energy to unexpected frequencies. In the four-row analysis, a region of unusually high unsteadiness near the tip of the LPT blade was also characterized by an increase in the amplitude of the fluctuating surface pressure by a factor of nearly seven, again, with unexpected attendant frequencies. A model is presented which explains the unexpected frequencies realized in the four-row results and allows the redetermination of these frequencies without the use of CFD. In an effort to better understand the complex interactions between the high- and low-pressure turbines, the first vane of the low-pressure turbine was redesigned, and the remaining airfoils were reoriented, to establish a counter-rotating turbine configuration. While substantial reductions in unsteady surface-pressure amplitudes were realized near the tip of the LPT blade with the switch to counter rotation, the amplitude of the temporally varying tangential load on the blade remained notably higher than that from the three-low analysis. The precise physical cause for the high levels of local unsteadiness near the tip of the first LPT blade in the co-rotating configuration remains unclear.

Object detection is an essential component of autonomous mobile robotic systems, enabling robots to understand and interact with the environment. Object detection and recognition have made significant progress using convolutional neural networks (CNNs). Widely used in autonomous mobile robot applications, CNNs can quickly identify complicated image patterns, such as objects in a logistic environment. Integration of environment perception algorithms and motion control algorithms is a topic subjected to significant research. On the one hand, this paper presents an object detector to better understand the robot environment and the newly acquired dataset. The model was optimized to run on the mobile platform already on the robot. On the other hand, the paper introduces a model-based predictive controller to guide an omnidirectional robot to a particular position in a logistic environment based on an object map obtained from a custom-trained CNN detector and LIDAR data. Object detection contributes to a safe, optimal, and efficient path for the omnidirectional mobile robot. In a practical scenario, we deploy a custom-trained and optimized CNN model to detect specific objects in the warehouse environment. Then we evaluate, through simulation, a predictive control approach based on the detected objects using CNNs. Results are obtained in object detection using a custom-trained CNN with an in-house acquired data set on a mobile platform and in the optimal control for the omnidirectional mobile robot.

The plantar pressure distribution has a complex influence on the kinetics and kinematics of the lower limbs. The foot bed-outsole ensemble must provide the correct support of the foot and add corrections if necessary, in order to adjust the pressure distribution on the foot plantar surface. A poorly designed outsole, a feeble midsole or the incorrect care and use of the footwear product will lead to the deterioration of the foot bed functionality. The degree of the foot bed deterioration can be determined using in-shoe plantar pressure measuring devices. This loss of functionality must be prevented as much as possible in the design stage. Footwear prototyping and wearing tests are very expensive and time consuming and do not represent a viable method in economic terms. The fastest and less expensive testing method suitable for footwear production in design stages is the Finite Element Analysis. To use this method we developed a 3D CAD model of the human foot using as model a real 3D scanned foot. The scanned foot was processed in various 3D CAD systems in order to obtain a FEA usable 3D part. The developed model was used to determine how the ensemble of a specific sole design and a midsole with wear characteristics modify the plantar pressure distribution.