Self-localization is critical for many unmanned aerial vehicles (UAVs) tasks such as formation flight, path planning, and activity coordination. Traditionally, UAV can locate itself using GPS combined with some inertial sensors. However, due to the complex flight environment or failure of the GPS receiver, the UAV may lose its GPS signal and fail to locate itself, resulting in devastating consequence. In this paper, we will consider the problem of cooperative localization among multiple UAVs, in which the UAVs with failure of GPS receiver can help each other to locate themselves through mutual information exchanged based on the relative distance measurements. Specifically, we propose a dynamic Nonparametric Belief Propagation (dNBP) algorithm to calculate the posterior distribution of UAV's position conditioned on all observations made in the whole UAVs group. The dNBP is a natural combination of NBP with particle filtering, suitable for treating with the nonlinear model and highly non-Gaussian distributions arising in our application. Furthermore, dNBP provides the basis for distributed algorithm in which messages are exchanges between neighboring UAVs. Thus, the computational burden is distributed across UAVs. Simulations in Matlab environment show the effectiveness of our method.
Wider linear modulation range, less switching loss, and simplicity are the goals pursued by various modulation methods of multilevel inverters. This paper proposes a new hybrid discontinuous pulsewidth modulation (HDPWM) strategy for three-level neutral-point-clamped (3L-NPC) inverter that can achieve the above goals to a certain extent. According to the position of the reference voltage vector and the actual situation of the neutral point voltage, different control modes and clamping types are selected. The neutral point voltage is controlled by DPWM strategy, which can not only greatly reduce the switch loss but also maintain the balance of the neutral point voltage and expand the linear modulation range. The implementation of the algorithm combines the advantages of carrier-based PWM (CBPWM) and space vector PWM (SVPWM). There is no need to select the nearest three vectors (NTVs) and calculate their dwell time. Only the reference voltage needs to be modified according to the control requirements, and then by comparing the modified reference voltage with the carrier, the driver pulse required by the switching devices can be generated. Compared with the existing PWM methods, it is more simple and easy to implement. Experimental results verify the validity of the method.clamping type, hybrid discontinuous pulsewidth modulation, neutral point voltage balancing, output level sequence type, space vector pulsewidth modulation
| INTRODUCTIONCompared with traditional two-level (2L) voltage source inverter (VSI), three-level neutral-point-clamped (3L-NPC) inverter has prominent advantages of simple structure, low total harmonic distortion (THD) of output voltage (or current), and low voltage stress on switching devices. 1-3 Therefore, since its introduction in 1981, 4 3L-NPC inverter has been used more and more widely, especially in medium-voltage applications.The neutral point (NP) voltage unbalance is an inherent drawback of 3L-NPC inverter, which is caused by the unbalanced charging and discharging of two DC link capacitors. The NP voltage unbalance includes AC low-frequency oscillation and DC offset. By increasing the capacitance of DC link capacitor, AC oscillation can be suppressed to a certain extent, but DC offset cannot be eliminated. Therefore, in comparison, the harm of DC offset is greater and must be avoided by other measures to ensure the stability and reliability of the inverter.
This article proposes a hybrid dynamic belief propagation for simultaneous localization and mapping in the mobile robot network. The positions of landmarks and the poses of moving robots at each time slot are estimated simultaneously in an online and distributed manner, by fusing the odometry data of each robot and the measurements of robot-robot or robot-landmark relative distance and angle. The joint belief state of all robots and landmarks is encoded by a factor graph and the marginal posterior probability distribution of each variable is inferred by belief propagation. We show how to calculate, broadcast, and update messages between neighboring nodes in the factor graph. Specifically, we combine parametric and nonparametric techniques to tackle the problem arisen from non-Gaussian distributions and nonlinear models. Simulation and experimental results on publicly available dataset show the validity of our algorithm.
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