On the basis of the classical computed torque control method, a new composite nonlinear feedback design method for robot manipulators with uncertainty is presented. The resulting controller consists of the composite nonlinear feedback control and robust control. The core is to use the robust control for online approximation of the system’s uncertainty as a compensation term for the composite nonlinear feedback controller. The design method of the new controller is given, and the convergence of the closed-loop system is proved. The simulation results show that the proposed scheme can make the uncertain robot system have strong robustness and anti-interference ability.

The electronic structure and the corresponding electrical conductive behavior of the Cu/Cr₂C/TiN stack were assessed according to a newly developed first-principle model based on density functional theory. Using an additional Cr₂C layer provides the metal-like characteristic of the Cu/Cr₂C/TiN stack with much larger electrical conduction coefficients (i.e., mobility, diffusivity, and electrical conductivity) than the conventional Ag/Ti₃C₂/Pt stack due to the lower activation energy. This device is therefore capable of offering faster switching speeds, lower programming voltage, and better stability and durability than the memristor device with conventional Ti₃C₂ MXene.

A single node failure capacity control function taking into account attack strength, attack times, control node load intensity and the degree of attacked node is proposed in this paper to mitigate the cascading failure of complex networks under random attack. An optimal probability allocation mechanism of redundant resources is established by targeting the load of each neighbor node. Then, the node failure capacity control function and allocation mechanism are used to define the phase transition critical factor and robustness indicator that used the attack strength, control node load intensity and the degree of attacked node as the parameters. Based on the above analysis, the phase transition critical factor model of degree distribution of scale-free network and random network is derived, and the dynamic change law between the parameters and phase transition critical state as well as robust performance of classical network and real network is analyzed. The theoretical and experimental results show that in the controllable region, the smaller the degree of attacked node, the greater the control node load intensity and the more difficult the phase transition critical state to be achieved, and the better the effect of mitigating cascading failure. Besides, the robustness of the network with cascading failure is mutually affected by the control node load capacity, the degree of attacked node and phase transition critical factor within a certain range, which thus embarks on a new perspective to mitigate the failure.

Machine game is one of the important directions of artificial intelligence research, and Chinese chess is a typical game process. This paper first introduces the game principle of Chinese chess. Then Q-learning method and evaluation function are added to train data through a large amount of self-learning. A chess game system based on Q-learning is designed and developed. The experimental results show that Chinese chess with Q-learning algorithm has the ability of evolutionary learning, which effectively improves the game level of Chinese chess.

With the development of network science, the coupling between networks has become the focus of complex network research. However, previous studies mainly focused on the coupling between nodes, while ignored the coupling between edges. We propose a novel cascading failure model of two-layer networks. The model considers the different loads and capacities of edges, as well as the elastic and coupling relationship between edges. In addition, a more flexible load-capacity strategy is adopted to verify the model. The simulation results show that the model is feasible. Different networks have different behaviors for the same parameters. By changing the load parameters, capacity parameters, overload parameters, and distribution parameters reasonably, the robustness of the model can be significantly improved.

The application of silicon carbide (SiC) MOSFETs in the field of high voltage and high frequency brings the major challenge of high switching loss. To give full advantage of its performance in high-frequency applications and provide a simulation analysis method for the analysis and design of power electronic systems, it is essential to establish simulation models of the SiC power MOSFET switching behavior suitable for different ambient temperatures. A temperature-dependent compact SPICE model considering parasitic parameters is proposed in this article, which uses only the parameters in the datasheet or provided by manufacturers. The main technology-dependent parameters are analyzed and discussed in detail, including the nonlinear parasitic capacitance, parasitic parameters of the power module, and static characteristic parameters. In static characteristics, a simple and continuous equation is used to describe the drain-source current of the SiC power MOSFET. The static characteristic parameters of the SPICE model were compared with the 1.2-kV SiC power MOSFET, manufactured by ROHM, Inc., (SCT3030KLHR). Subsequently, the dynamic characteristic is verified by comparing the simulation results with experimental results in a double pulse circuit employing the half-bridge module under different temperatures. Comparisons between the datasheet and experiments demonstrate the precision of the modeling methodology and the consistency of the results.