In this paper, in order to optimize the dynamic performance of the permanent magnet synchronous motor (PMSM) speed regulation system, a nonlinear speed-control algorithm for the PMSM control systems using sliding-mode control (SMC) is developed. First, a sliding-mode control method based on a new sliding-mode reaching law (NSMRL) is proposed. This NSMRL includes the system state variable and the power term of sliding surface function. In particular, the power term is bounded by the absolute value of the switching function, so that the reaching law can be expressed in two different forms during the reaching process. This method can not only effectively suppresses the inherent chattering, but also increases the velocity of the system state reaching to the sliding-mode surface. Based on this new reaching law, a sliding-mode speed controller (SMSC) of PMSM is designed. Then, considering the large chattering phenomenon caused by high switching gain, an improved anti-disturbance sliding-mode speed controller(ADSMSC) method, called SMSC+ESO method, is developed. This method introduces an extended state observer (ESO) to observe the lumped disturbance and adds a feedforward compensation item based on the observed disturbances to the SMSC. Finally, simulation and experimental results both show the validity of the proposed control method.

This paper proposes a novel T-type multilevel inverter (MLI) based on the switched-capacitor technique. The proposed inverter not only achieves that the maximum voltage stress of the switches is less than the input voltage but also has a voltage boost capability, which makes it suitable in high voltage applications. It is worth mentioning that the proposed inverter features two topology extension schemes which help it achieve a higher output level and voltage gain. With the merit of low voltage stress and reduced power devices, a seven-level inverter can be achieved using only two capacitors. Moreover, capacitor voltage self-balancing capability can simplify the complexity of the circuit and control. The topology, operating principle, modulation strategy and analysis of the capacitor of the inverter are presented. The superiorities of the proposed inverter are investigated by comparing with recently proposed hybrid MLIs and switched-capacitor MLIs. Finally, a seven-level prototype is constructed to validate the correctness of the theoretical analysis and the feasibility and effectiveness of the proposed inverter.

Two-vector model predictive current control (MPCC), which applies two vectors in a control cycle, has better steady state performance than conventional MPCC. However, two-vector MPCC needs to select two optimal vectors, calculate two current slopes and two vector working times, which have to use the inductance and the flux linkage parameter in the permanent magnet synchronous motor (PMSM) drives. Therefore, the control performance of two-vector MPCC is more influenced by the model parameter accuracy. Aiming at reducing parameter sensitivity of the two-vector MPCC method, a robust two-vector MPCC method is proposed, which can obtain accurate inductance and flux linkage information in real time based on the presented inductance extraction algorithm and the flux linkage extraction algorithm. Moreover, the proportional-integral regulator parameters of the extraction algorithms are theoretically deduced. The experiment results of the proposed method indicate that the satisfactory control performance can be achieved under the condition of the parameter mismatches.

Deadbeat direct torque control (DBDTC) calculates the voltage vector based on the motor mathematical model and tracks the torque and flux reference within only one sampling cycle. However, in the traditional DBDTC, the reference torque is generated by a speed PI controller, which presents a low dynamic and poor precision, particularly under external disturbances. To sort out this issue, this article proposes an improved DBDTC control method basing on the sliding mode strategy. First, an antidisturbance sliding mode controller (ASMC) is presented which is superior in offering a fast and accurate reference torque for DBDTC. Along the way, an extended sliding mode disturbance observer is introduced, which estimates total disturbances and compensates the sliding mode controller. To reduce the chattering of sliding mode control, a novel reaching law is proposed. This novel reaching law introduces a system-state variable in the exponential terms of power reaching law and, meanwhile, includes an adaptive exponential reaching action. By this means, it increases system convergence rate to the sliding mode surface while suppressing sliding mode chattering. Finally, both simulation and experimental results show that the proposed control method has a better performance in terms of torque ripple reduction and speed dynamic response.

In order to improve the anti-disturbance performance of permanent magnet synchronous motor (PMSM) servo system, a sliding-mode control strategy using a new reaching law (NRL) is proposed. The NRL incorporates power term and switching gain term of the system state variables into the conventional exponential reaching law (CERL), which can effectively suppress the sliding-mode chattering and increase the convergence rate of system state reaching sliding-mode surface. Based on this new reaching law, a sliding-mode speed controller (SMSC) of PMSM is designed. At the same time, to solve the chattering problem caused by the large sliding-mode switching gain, an anti-disturbance sliding-mode speed controller method with an extended sliding-mode disturbance observer (ESMDO), called SMSC+ESMDO method, is developed. The sliding-mode disturbance observer is designed to accurately estimate the motor speed and external load disturbances, and the disturbance estimator is used as a feedforward to compensate the sliding-mode speed controller (SMSC) to improve the system robustness and reduce the system chattering. Simulation and experimental results show that the proposed compound sliding-mode control strategy can effectively improve the dynamic performance and robustness of the system compared with the PI controller.

Accurate estimation of dynamic states is the key to monitoring power system operating conditions and controlling transient stability. However, the inevitable non-Gaussian noise and randomly occurring denial-of-service (DoS) attacks may deteriorate the performance of standard filters seriously. To deal with these issues, a novel resilient cubature Kalman filter based on the Cauchy kernel maximum correntropy optimal criterion approach (termed CKMC-CKF) is developed, in which the Cauchy kernel function is utilized to describe the distance between vectors. Specifically, the errors of state and measurement in the cost function are unified by a statistical linearization technique, and the optimal estimated state is acquired by the fixed-point iteration method. Due to the salient thick-tailed feature and the insensitivity to the kernel bandwidth of Cauchy kernel function, the proposed CKMC-CKF can effectively mitigate the adverse effect of non-Gaussian noise and DoS attacks with a better numerical stability. Finally, the efficacy of the proposed method is demonstrated on the standard IEEE 39-bus system under various abnormal conditions. Compared with standard cubature Kalman filter (CKF) and maximum correntropy criterion CKF (MCC-CKF), the proposed algorithm reveals better estimation accuracy and stronger resilience.