Search

Rotor unbalance (RUB) and Sensor Runout (SR) are two main vibration sources for active magnetic bearing (AMB) system, and they not only produce considerable harmonic vibration but also endanger the stability of AMB system. Therefore, a nonlinear adaptive algorithm, whose asymptotic stability is guaranteed by Lyapunov’s theory, is proposed in this paper to suppress the harmonic vibration. Nevertheless, RUB and the synchronous component of SR have the same frequency with the rotational frequency, and it is highly necessary to distinguish them from each other for the compensation of displacement stiffness force. Hence, the scheme of changing rotor speed is adopted for the permanent magnet biased magnetic bearing system. As a consequence, the harmonic current caused by RUB and SR is effectively attenuated and the displacement stiffness force is correctly compensated. The results of the numerical simulations and experiments demonstrate that the harmonic vibration is dramatically suppressed through the proposed method.

Driverless automatically guided vehicles are becoming a new trend in transportation envisioned in a smart city environment. Automatically guided vehicles depend on environmental data for executing target-oriented navigation and movements. The traffic knowledge can be used by automatically guided vehicles by measuring their intensity and delivery date in the green light. Therefore, considering sensor data's significance in this guided vehicle environment, this article presents the Internet of Things (IoT) assisted automated guided vehicle (IoT-AGV) scheme. IoT-AGV scheme discusses the implementation of an autonomous vehicle traffic control program at crossings. The traffic knowledge operator is ready to retrieve details from the sensor utilizing quick communication through a mobile network to measure optimum travel time and transmit the information collected to automated cars. In an automated guided vehicle (AGV) scheme, the automotive-into-loop model is introduced to eliminate delays. Error indicator integrates various hurdle imbalance statistics calculated from the sensor based on the road slide. A deep neural network focuses on a minimal collection of the most critical features, eliminating the fitting problem. This aids determine the error producing sensor data and alleviate them from fusion to enhance the target's accuracy. The experimental results show that the suggested system has been validated by the images from BIT vehicle datasets and enhances the accuracy ratio of 96.22% to measure optimum travel time and transmit the information collected to automated guided vehicles.

Location awareness and navigation promote varieties of emerging applications of mobile collaborative multiple uncrewed aerial vehicles (UAVs). Cooperative UAVs fuse the global position system (GPS), inertial navigation systems (INS), peer to peer ranging radios derived from relative navigation of ultra-wideband (UWB) under complicated environments. Those information sources can be incorporated into spatiotemporal cooperation posited by the intra-user measurement of INS and GPS, and the inter-user measurement of the relative navigation of swarm UAVs. This paper considers the localization and navigation of multiple collaborative UAVs in networks with GPS/INS/UWB jammers in the case that the measurements are missed or randomly delayed by a sampling period. In a navigation situation with a partially denied navigation signals (e.g.GPS Jammers for some UAVs, UWB jammers for others, etc.), we propose an improved method of cooperation location for the swarm, allowing measurement jammers concerning the normal sigma point belief propagation (SPBP). This algorithm integrates message passing based on the Bayesian framework, a sigma point belief propagation of random packet loss (SPBP-RPL) to exploit spatiotemporal cooperation and measurement knowledge. Compared with existing general sigma point belief propagation, the advantages of the novel method are validated through a simulation of swarm UAVs with GPS/INS/UWB. Results show that the algorithm of combining spatiotemporal cooperation with measurement knowledge reduces the location uncertainty of swarm UAVs agents and improves location accuracy remarkably.

Rack and pinion drives (RPDs) are key components of battery-swapping systems (BSSs) used in electric heavy trucks; the faults occurring in these drives reduce the efficiency, accuracy, quality of battery swapping, and even pose potential safety risks. The operating conditions of RPD driving gear in BSSs are characterized by speed fluctuations, relatively low speeds, and reciprocating motion. To assess the driving gear fault characteristics under these conditions, based on the solution of image recognition under complex and low illumination conditions, this study proposes a fault diagnosis framework that includes adaptive down-sampling, three-dimensional acceleration data fusion, multi-scale local binary pattern (MS-LBP) extraction, and sparse representation. First, adaptive down-sampling is used to smooth out the speed fluctuation. Subsequently, MS-LBP extraction is employed to obtain efficient fault features at low speed. Finally, dictionary learning and sparse representations are conducted on the MS-LBP features. The effectiveness and advantages of the proposed diagnosis approach are demonstrated using monitoring data acquired from a BSS. Moreover, comparative studies demonstrate that the proposed fault diagnosis method yields improved performance.

A novel robust scheme for Transfer Alignment (TA) is proposed for improving the accuracy of the navigation of a Hypersonic Cruise Vehicle (HCV). The main goal is to instil robustness in the safety and accuracy of the attitude determination, despite mode uncertainties. This article focuses on Robust Sparse-Grid Quadrature Filtering (R-SGQF) using two given robust factors for norm-bounded model uncertainties in non-linear systems. Missile dynamic and measurement model uncertainties are established to validate TA technologies. The nominal stability of the R-SGQF is defined by estimating error dynamics. The technique gives sufficient conditions for the R-SGQF in terms of two parameterised Riccati equations. Robust stability is analysed using Lyapunov theory and the accuracy level of the Sparse-Grid Quadrature (SGQ) algorithm. Embedding the SGQ technique into the robust filter structure, R-SGQF is not only of robust stability against uncertainty but also of higher accuracy. The simulation results of the TA algorithm demonstrate that attitude determinations validate the effectiveness of the R-SGQF algorithm.

This article aims at improving positioning precision and stability of the SINS/Doppler/Platform Compass integrated navigation system. Firstly, based on considering of the characteristic and performance of the integrated system, the system model is established. And the discrete state and measurement equations are achieved. Secondly, traditional Kalman filter for this system is usually unstable, and easy to divergent. So reasons for divergence of integrated navigation with traditional Kalman filter is analyzed. Thirdly, Sage-Husa Kalman filter based on UD decomposition is designed. And some improvements have been made according to the analysis results. Finally, several simulation experiments were made to test the effect of the improved algorithms. The simulation results indicate that improved Sage-Husa Kalman filter based on UD decomposition can guarantee the precision and stability of the SINS/Doppler/Platform Compass integrated navigation system.

In the initialization process of the strapdown initial navigation system (SINS) of a launched weapon before its release, transfer alignment (TA) uses the data from the aircraft SINS or other navigation aids. However, stochastic measurement aid information is unreliable in practical integrated navigation system. The paper is the filtering stability for a class of nonlinear stochastic systems with intermittent measurements when the arrival of the observations is described by Bernoulli independent and identically distributed process. The stochastic stability of the a novel Gaussian filter through presenting Gaussian approximate about one step posterior predictive probability density function (PDF) of the state and intermittent measurements is investigated. The estimation errors remain bounded if the system satisfies some sufficient conditions, and its error covariance matrices are statistically convergent by providing the existence of a super-threshold value for the intermittent measurement probability. Finally, a nonlinear integrated navigation method based on the proposed filter is presented, and the ground vehicle test of missile-board SINS is given to illustrate the effectiveness of the investigated techniques.