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The output voltage distortion of the inverter worsens the performance of the permanent-magnet synchronous motor, therefore, it should be compensated accurately. The inverter output voltage can be measured by the oversampling system, but the measured voltage contains drift and noise. To solve this problem, an adaptive sigmoidal function (S-function) model is first proposed in this article to estimate the voltage distortion. A nonlinear Levenberg–Marquardt regression method with voltage oversampling measurement is employed to identify the initial parameters of the adaptive S-function. During the operation, a recursive least square method is exploited to refresh the parameters of the S-function in realtime. The voltage distortion is accurately obtained by the proposed method, and an accurate compensation scheme is proposed to reduce the current distortion and torque ripple. The effectiveness of the proposed method is validated by experiments.

In order to improve the performance of interior permanent magnet synchronous machine (IPMSM) based on space voltage vector modulation schemes, this paper presents a highly accurate online method to find the proper maximum-torque-per-ampere (MTPA) angles. This algorithm fully considers the nonlinear characteristics of the d -axis inductance, q -axis inductance, and permanent magnetic flux together. Additionally, the influences of temperature are taken into account. In this paper, the proposed method injects a small virtual angle signal into the current angle mathematically to track the stator current reference angle that is close to real MTPA points. Moreover, following a theoretical analysis method, the error of the virtual signal injection method is analyzed. Then, based on the results of error analysis, this paper proposes a compensation strategy that can significantly reduce the current angle error between the tracking point and real MTPA point. At last, various experiments on an IPMSM test bench verify the proposed control technique and overall performance.

During transient process, it may take plenty of steps for PMSM to reach the reference torque and flux linkage. Multistep model predictive torque and flux control (MPTC) can select the best sequence of voltage vectors from the initial state to the reference state to achieve the fastest torque response. However, the computation burden is a significant challenge for real-time implementation. This paper proposes a novel fast-response MPTC strategy for PMSM drives. The reference torque is converted to flux linkage in d -axis and q -axis to eliminate the weighting factor in cost function. During the transient process, Pontryagin's maximum principle is used to design minimum-time flux linkage trajectories from the initial state to the reference state. In each control period, along the reference flux linkage trajectories, one-step MPTC is used to select the best voltage vector. The computational burden and dynamic performance of the proposed method are verified by experiments.

Fast and accurate visual scene understanding in autonomous vehicles is necessary but still very challenging. An autonomous vehicle must be taught to read the road like a human driver for better controlling the vehicle, so it is important to efficiently detect the road area and road markings. In this paper, we mainly focus on the vanishing point detection and its application in inverse perspective mapping (IPM) for road marking understanding. We first propose a fast and accurate vanishing point detection method for various types of roads, by adopting and improving Weber local descriptor to obtain salient representative texture and orientation information of the road area, and then voting for the dominant vanishing point with a simple line-voting scheme. Experimental results demonstrate that the proposed vanishing point detection approach gains a better performance than some state-of-the-art methods in terms of accuracy and computation time. Furthermore, we introduce the detected vanishing point into the IPM algorithm in the structured road environment, since some important calibration parameters can be automatically calculated by the vanishing point, especially on the rough road. Experiments also show that our proposed vanishing point-based IPM method is adaptive and accurate, which is conducive to the subsequent road marking detection and recognition.

This paper proposes a robust inclinometer system using three monaxial microelectromechanical systems accelerometers and three monaxial fluxgate sensors. By formulating a basic three sensitive axes sensor model, we calibrate tilt and azimuth directly through a simple and effective linear model. To improve the accuracy of the proposed model, we present two different optimal solutions to minimize the systematic error, and adopt the interior-reflective Newton method and the sequential quadratic programming method to solve the problems, respectively. Experimental results demonstrate that our system performs excellently with the maximum error of tilt angle 0.09 $^{\circ}$ in our applied measurement range (0 $^{\circ}$ –120 $^{\circ}$ ), and the maximum error of azimuth angle 0.4 $^{\circ}$ in the measurement range (0 $^{\circ}$ –360 $^{\circ}$ ).

We propose a tilt sensing scheme using a physical model with three sensitive axes of microelectromechanical systems (MEMS) accelerometers. Based on the physical model in which the gravitational acceleration is resolved into three components, we propose three numerical models to sense the tilt angle. First, two exact numerical models are presented to measure the gravitational acceleration and its one component along the tilt direction, respectively. The parameters of these two models are specific angles of the physical model, which can be used to assess the configuration of the physical model. Next, the measurement bias model is introduced to reduce the error resulting from the nonlinear relation between the gravitational acceleration and the tilt angle. Third, all the three numerical models are unified into a linear model whose parameters can be efficiently estimated using the least squares method. In the experiments, we evaluate the performance of the proposed scheme by examining the mean, the standard deviation, and the maximum of the errors. The experiment results show that our scheme is able to perform accurate tilt sensing with the average error below 0.1° in the measurement range (0°, 120°).