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At present, the influence of time-harmonic currents on the leakage flux coefficient of canned permanent magnet synchronous motors (CPMSMs) remains unclear. Therefore, this paper investigates and summarizes the effects of inverter time-harmonic currents on the leakage flux coefficient of the CPMSM. A 1.5 kW, 9000 RPM (Revolutions Per Minute) CPMSM is used as the research subject. Based on a finite element model, the leakage flux coefficients under sinusoidal and non-sinusoidal supply conditions are calculated using the magnetic flux density integration method. Additionally, the study examines changes in the leakage flux coefficient under different harmonic orders and amplitudes. The results indicate that the introduction of fifth and seventh time-harmonic currents by the inverter reduces the leakage flux coefficient, which helps to improve the utilization of the permanent magnets. This research significantly contributes to enriching the theory of inverter-fed permanent magnet motors. [ABSTRACT FROM AUTHOR]

This paper proposes a new type of echo state network called Long‐Term Memory Enhanced Echo State Network (LTME‐ESN). By extending the ideas of the forget gate and the input gate from the LSTM, LTME‐ESN modifies the equation for updating the state in a Leaky Integral Echo State Network (Leaky‐ESN). By regulating the accumulation of information, the construction of extended periods of dependency between states may be delayed, allowing for adaptive management of the data transported from the previous state to the present one. In order to optimize parameters by using Stochastic Gradient Descent (SGD), this research presents a necessary condition for LTME‐ESN to meet the properties of the echo state network. The study uses low‐frequency sinusoidal, high‐frequency sinusoidal, and chaotic time series to demonstrate the model's efficiency. Simulations show that LTME‐ESN outperforms Leaky‐ESN in prediction accuracy and variation. [ABSTRACT FROM AUTHOR]

In this paper, a proposed echo state network (ESN) model called F‐ESN is based on methane topology. Due to the neurons in the reservoir of the ESN model being random and sparsely connected, the stability of the system structure could be better, and the prediction accuracy could be higher. To solve this issue, the reservoir's neuronal connectivity is modified to a certain methane structure. A master neuron governs each little unit and communicates with the others to pass information. In addition, MFO optimized by adaptive dynamic operators is used to optimize the three parameters of ESN : leakage rate (a)$ (a)$, spectral radius of the connection weight matrix (ρ)$ (\rho)$, and input scale factor (sin)$ (s^{in})$. To verify the effectiveness of the proposed method, low‐frequency sin time series, high‐frequency SIN time series, and MG time series are simulated. The experimental results show that the methane topology can further improve the prediction accuracy of leakage echo state networks. [ABSTRACT FROM AUTHOR]

The paper expounds the question of echo state network‐based fixed‐time adaptive tracking control for a type of multi‐input multi‐output nonlinear strict‐feedback system with input delayed saturation. Based on the feature that echo state network can obtain better estimation performance at lower computational cost, it is utilized to approximate the unknown nonlinear function in the controller design course. By constructing an auxiliary system, a time‐delay system with input saturation is eliminated. Moreover, command filtering techniques can be applied to avoid the "complexity explosion" problem in the backstepping method. Then, the closed‐loop system is attested to be semi‐global practical fixed‐time stable. In the end, a simulation shows the valid scheme. [ABSTRACT FROM AUTHOR]

The heat generated inside solar cells is one of the important factors influencing the operational stability of a photovoltaic system composed of solar cells. In this work, based on the influencing factors of four main heat-generated mechanisms inside c-Si solar cells, i.e., the energy relaxation process of the hot carrier, Joule heat, recombination heat, and Peltier and Thomson effects, the distribution features of the heat generated by each mechanism are first discussed, and then, considering two common working conditions of c-Si solar cells in module form, i.e., the variations of environmental temperature and output voltage, the changing rules of the distributions of heat generated by different mechanisms are analyzed. The general features of the heat distributions of c-Si solar cells are that the obviously absorbed and released heat only exists in the regions near the front and back surfaces; the four mechanisms all release heat in the narrow n region close to the front surface; and Peltier and Thomson effects are the only mechanism of absorbed heat in the wide p region. The offset effects between released and absorbed heat make the total heat generated by the four mechanisms very small in the wide p region. With the environmental temperature increasing, the heat distribution features of each heat mechanism will remain unchanged, and except for the heat generated by the energy relaxation of the hot carrier, the released and absorbed heat generated by other mechanisms will increase in the whole c-Si active layer. With the output voltage increasing from 0 to 0.68 V, aside from the heat distribution of the energy relaxation of the hot carrier, the distributions of heat generated by other mechanisms show obvious threshold voltage effects, and only when the output voltage is larger than the threshold voltage will the heat distributions of different mechanisms be significantly changed by the variations of the output voltage. [ABSTRACT FROM AUTHOR]

This paper investigates the observer-based adaptive stabilization control problem for a class of time-delay nonlinear systems with unknown control gain using an echo state network (ESN). In order to handle unknown functions, a new recurrent neural network (RNN) approximation method called ESN is utilized. It improves accuracy, reduces computing cost, and is simple to train. To address the issue of unknown control gain, the Nussbaum function is used, and the Lyapunov–Krasovskii functionals are used to address the delay term. The backstepping strategy and command filtering methodology are then used to create an adaptive stabilization controller. All of the closed-loop system's signals are predicted to be confined by the Lyapunov stability theory. Finally, a simulation example is used to demonstrate the effectiveness of the suggested control mechanism. [ABSTRACT FROM AUTHOR]