In this paper, an optimal frequency control approach is proposed for VSG-based AC Microgrids (MGs) to improve the frequency regulation performance by considering the nonlinear dynamics. First, a nonlinear dynamics system of the MG are analytically modelled by using the VSG controller. Following, the optimal controller is designed based on the Hamilton-Jacobi-Bellman (HJB) equation and is obtained by using the adaptive dynamic programming (ADP) method without linearizing. Then, a single neural networks (NNs) construction is used to approximate the optimal controller and cost function, simultaneously. Unlike the conventional VSG approach with constant inertia, the novel method can determine the optimal inertia in an online and adaptive way, as well as preserve a balance between the frequency regulation performance and the control cost. Finally, simulation results based on MATLAB/Simulink verify that the proposed control method improves the frequency nadir and the rate of change of the frequency (RoCoF) while DC-side energy is also drastically preserved by comparing the existing three studies. Note to Practitioners —a) Previous methods always assume that the generator can instantly generate and absorb infinite power, however, capacitance is finite in practice. This paper proposes an adaptive VSG-based method to provide a bounded threshold of inertia, which facilitates practical applications. b) The AC MG system is modeled as a nonlinear system to cope with the strong disturbance, which is more suitable for practical system. c) The controller is approximated by NN, which is designed by three physical variables, i.e., angular deviation, frequency deviation and rate of change of the frequency (RoCoF), which can be measured from the practical system. d) Small signal state space model is used to analyze the stability of the controller, which further proves the stability of the proposed method and enhances its application prospect in engineering practice.

The problem of the large-scale interconnected system (LSIS) control is prevalent in practical engineering and is becoming increasingly complex. In this article, we propose a novel decentralized event-triggered tracking control (ETTC) strategy for a class of continuous-time nonlinear LSIS with unmatched interconnected terms and asymmetric input constraints. First, auxiliary subsystems are established to address the unmatched cross-linking terms. Next, the dynamics states of the tracking error and the exosystem are combined to construct a nominal augmented subsystem. By employing a nonquadratic performance function, the input-constrained decentralized tracking control problem is transformed into an optimal control problem for the nominal augmented subsystem. A group of independent parameters and event-triggered conditions are designed to save communication bandwidth and computational resources. Subsequently, the critic-only adaptive dynamic programming (ADP) method is used to solve the Hamilton-Jacobi–Bellman equation (HJBE) associated with the optimal control problem. To improve training success rate, the weights of the critic neural network (NN) are updated by introducing a particle swarm optimization algorithm (PSOA). The tracking error and the NN weights are proved to be uniformly ultimately bounded (UUB) under the proposed ETTC by using the Lyapunov extension theorem. Finally, the simulation example of an unmatched interconnected system is provided to verify the validity of the proposed decentralized method.

The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA). But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real‐time performance, and low reliability. In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS) when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.

The three-dimensional (3D) diamond-like semiconductor materials Si-diamondyne and Ge-diamondyne (also called SiC₄ and GeC₄) are studied utilizing density functional theory in this work, where the structural, elastic, electronic and mechanical anisotropy properties along with the minimum thermal conductivity are considered. SiC₄ and GeC₄ are semiconductor materials with direct band gaps and wide band gaps of 5.02 and 5.60 eV, respectively. The Debye temperatures of diamondyne, Si- and Ge-diamondyne are 422, 385 and 242 K, respectively, utilizing the empirical formula of the elastic modulus. Among these, Si-diamondyne has the largest mechanical anisotropy in the shear modulus and Young’s modulus, and Diamond has the smallest mechanical anisotropy in the Young’s modulus and shear modulus. The mechanical anisotropy in the Young’s modulus and shear modulus of Si-diamondyne is more than three times that of diamond as determined by the characterization of the ratio of the maximum value to the minimum value. The minimum thermal conductivity values of Si- and Ge-diamondyne are 0.727 and 0.524 W cm⁻¹ K⁻¹, respectively, and thus, Si- and Ge-diamondyne may be used in the thermoelectric industry.