Identifying the air-gap flux is a prerequisite for realizing the decoupling control between the air-gap flux and torque in an electrically excited synchronous motor (EESM) vector control system. For the nonlinear problem of EESM, the frequency response function method is introduced to analyze the air-gap flux voltage and current models' sensitivity to the motor parameters in this article. A dynamic air-gap current model for EESM based on the equivalent reaction inductance is proposed to solve the parameter dynamic-variant problem caused by the nonlinear magnetic distribution of the EESM. Meanwhile, the comparative analysis of effectiveness caused by linear and nonlinear flux identification errors in the hybrid air-gap flux model of vector control system is carried out. The effectiveness of the nonlinear dynamic model has been verified by simulation and experimental results, which shows its excellent control performance.

This paper proposes a novel control strategy for controlling the uninterruptible power supply (UPS) inverter, which is based on backstepping control theory combined with a fuzzy neural network (FNN). The advantage of backstepping control is that it can decompose a complex system into multiple subsystems, stabilize the control object according to Lyapunov stability theory, and simplify the controller design. However, it requires prior knowledge of multiple system parameters. FNN can approximate arbitrary nonlinear functions and system errors, which can reduce the parameters required for controller design. Hence, Combining the advantages of both methods, a UPS inverter control method with only a few parameters is designed. Then the sliding mode gain is added to compensate for the fuzzy neural network to reduce the chattering when the system operates and ensure the needed power quality. To verify the effectiveness of the proposed control system, the effectiveness of the proposed method is verified by a simulation experiment platform.

Carbon nanotube-based ionizing gas sensors are susceptible to fluctuations in gas detection results due to variations in temperature, necessitating temperature compensation to align with real-world application standards. This paper presents an experimental determination of the single-value sensitivity of the sensor to hydrogen (H2), where the collecting current of the sensor was observed to decrease with rising hydrogen concentration and temperature. We introduce a novel temperature compensation model based on the Least Squares Support Vector Machine optimized by the Whale Optimization Algorithm (WOA-LSSVM), which executes temperature compensation concurrently with hydrogen concentration detection. Post-implementation of temperature compensation, there has been a significant reduction in the temperature influence coefficient of the sensor by three orders of magnitude, resulting in a substantial enhancement in the sensor’s detection performance.

The limitation of DC current injection to grid is critical for transformerless grid-connected PV inverter. In this paper a novel control strategy for three phase PV inverter is proposed in order to suppress DC current injection to the grid. It is based on idea of accurately sensing the DC offset voltage of three-phase PV inverter output. Since each one of the DC components in the three-phase inverter output can be eliminated, DC injection current to the grid can be effectively suppressed. The control scheme is verified on a 20kW three phase PV inverter.

Finite-set model predictive controls have been widely used in inverter control because of the flexible target control and no need of a modulation unit. However, the mismatching of prediction model parameters produces prediction errors, resulting in a significant decline in the performance of finite-set model predictive controls. Aiming at the problem of model parameter mismatch, an extended-state observer was proposed to accurately estimate the disturbance of the system in this paper, and the obtained disturbance value was added to a finite-set model predictive control controller to compensate for the prediction error and achieve parameter robustness. By constructing a prediction model of the inverter output voltage in αβ coordinates, all the possible output voltage values were predicted by using different voltage vectors and system measurement values. A set of voltage vectors that minimized the cost function was selected, and its corresponding switching state was applied to the inverter in the next sampling period to achieve control of the output voltage quality. Both the simulation and experimental results showed that the finite-set model predictive voltage control method based on the extended-state observer can estimate the total disturbance quickly and accurately, suppress the influence of capacitance parameter disturbance, and improve the control effect of an inverter.

A grid-connected inverter’s stability is easily influenced by the system’s internal and external characteristics, especially when the grid-connected inverter is connected to the grid by an LCL filter, which complicates the system design and operating conditions. To handle the problem of disturbance in the high-order grid-connected system of the inverter, a linear active disturbance rejection control (LADRC) strategy based on LCCL (which divides the capacitor of the LCL filter into two halves in a specified proportion) is proposed in this paper. First, the system is reduced from third order to first order by using the split capacitor control method, which solves the difficulties of complicated LCL grid-connected inverter controller design and easy resonance. Meanwhile, the internal and external disturbances of the inverter system are treated as generalized disturbances. Then, the LADRC control is used to adjust the closed-loop system’s parameters, which enhances the quality of the grid current and the system’s stability. Finally, the impacts of PI and LADRC control on the power mutation and voltage drop of an LCCL grid-connected inverter are compared by the experiments. The experimental results show the effectiveness of the proposed LADRC control strategy.