Parallel Optimal Control for Cooperative Automation of Large-scale Connected Vehicles via ADMM

This paper proposes a parallel optimization algorithm for cooperative automation of large-scale connected vehicles. The task of cooperative automation is formulated as a centralized optimization problem taking the whole decision space of all vehicles into account. Considering the uncertainty of the environment, the problem is solved in a receding horizon fashion. Then, we employ the alternating direction method of multipliers (ADMM) to solve the centralized optimization in a parallel way, which scales more favorably to large-scale instances. Also, Taylor series is used to linearize nonconvex constraints caused by coupling collision avoidance constraints among interactive vehicles. Simulations with two typical traffic scenes for multiple vehicles demonstrate the effectiveness and efficiency of our method

Graphene controlled Brewster angle device for ultra broadband terahertz modulation

Terahertz modulators with high tunability of both intensity and phase are essential for effective control of electromagnetic properties. Due to the underlying physics behind existing approaches there is still a lack of broadband devices able to achieve deep modulation. Here, we demonstrate the effect of tunable Brewster angle controlled by graphene, and develop a highly-tunable solid-state graphene/quartz modulator based on this mechanism. The Brewster angle of the device can be tuned by varying the conductivity of the graphene through an electrical gate. In this way, we achieve near perfect intensity modulation with spectrally flat modulation depth of 99.3 to 99.9 percent and phase tunability of up to 140 degree in the frequency range from 0.5 to 1.6 THz. Different from using electromagnetic resonance effects (for example, metamaterials), this principle ensures that our device can operate in ultra-broadband. Thus it is an effective principle for terahertz modulation

Recycling spent lead-acid battery into lead halide for resource purification and multifunctional perovskite diode

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A dual control strategy for power sharing improvement in islanded mode of AC microgrid

Abstract Parallel operation of inverter modules is the solution to increase the reliability, efficiency, and redundancy of inverters in microgrids. Load sharing among inverters in distributed generators (DGs) is a key issue. This study investigates the feasibility of power-sharing among parallel DGs using a dual control strategy in islanded mode of a microgrid. PQ control and droop control techniques are established to control the microgrid operation. P-f and Q-E droop control is used to attain real and reactive power sharing. The frequency variation caused by load change is an issue in droop control strategy whereas the tracking error of inverter power in PQ control is also a challenge. To address these issues, two DGs are interfaced with two parallel inverters in an islanded AC microgrid. PQ control is investigated for controlling the output real and reactive power of the DGs by assigning their references. The inverter under enhanced droop control implements power reallocation to restore the frequency among the distributed generators with predefined droop characteristics. A dual control strategy is proposed for the AC microgrid under islanded operation without communication link. Simulation studies are carried out using MATLAB/SIMULINK and the results show the validity and effective power-sharing performance of the system while maintaining a stable operation when the microgrid is in islanding mode

Randomized incremental least squares for distributed estimation over sensor networks

This paper proposes a randomized incremental algorithm to distributedly compute the least square (LS) estimate of linear systems over sensor networks. By integrating its measurement information, a sensor is randomly activated at every time to incrementally update a diffusion vector, which is also used to recursively estimate the unknown parameters of the system via a temporal average algorithm. Then, the updated diffusion vector is passed to the next activated sensor. The activating process is modeled as an identically and independently distributed process. It is shown that the estimate in each sensor asymptotically converges both in mean and almost surely to the standard LS estimate of the system parameters, which is based on all the sensor information. Simulation is finally included to validate the theoretical results. © IFAC

A fractional time‐step simulation method suitable for the associated discrete circuit model of power electronic system

Abstract The associated discrete circuit (ADC) model for switches is widely used in electromagnetic transient simulation of power electronic system. When using ADC model, the problem of virtual power loss caused by L/C switching cannot be ignored and small time‐step (≤2 μs) is usually required to guarantee accuracy, both of which will limit the application of ADC model. To cope with this, a fractional time‐step simulation method (FTSSM) suitable for the ADC model of power electronic system is proposed. The method is derived from the compact form of electro‐magnetic transient program (EMTP) algorithm, and the simulation iteration is implemented by small‐step synthesis calculation with different fractional time‐steps, which are designed to locate the accurate switching actions. In this way, the simulation can be conducted under larger time‐step (to tens of μs) while the accuracy is still acceptable. Further analysis on time domain error and numerical stability of the FTSSM are discussed. Simulations of grid‐connected inverter system and permanent magnet synchronous motor (PMSM) driving system are done, using FTSSM and on PSCAD respectively for comparison. The results verify the accuracy of proposed FTSSM, and indicate that the FTSSM is helpful to accelerate the offline simulation and improve the real‐time simulation performance