In this paper, a sensorless field-oriented vector control (FOC) strategy combined an improved adaptive sliding mode observer (IASMO) and a back electromotive force (back-EMF) observer is presented for a surface permanent magnet synchronous motor (SPMSM). Firstly, based on a sliding mode, a current adaptive observer with estimation of back-EMF is proposed to obtain the error of the back-EMF, which can reduce system chattering. Then a back-EMF observer based on the back-EMF error is constructed to adaptively track the rotor speed and position, which avoids complicated computation. Moreover, the stability of two observers are proved by using the Lyapunov stability theory. Finally, some simulations and experiments are given to show that the proposed method can track the changes of rotor speed and position with good characters, such as fast convergence, little chattering and good robustness.

This paper proposes a current decoupling control strategy by combining a fast terminal sliding mode control (FTSMC) and an adaptive extended state observer (AESO). Firstly, the voltage errors and external disturbances caused by d-q axis current coupling and motor parameter variations are regarded as the lumped disturbance of the system, which facilitates the decoupling of d-q axis currents. Next, an equivalent control-based the FTSMC strategy is used to track the d-q axis currents to obtain the proper d-q axis voltages and improve the current dynamic performance. Then, the AESO method is utilized to observe the lumped disturbance and compensate for the input signal. Afterwards, the FTSMC and AESO algorithms are combined to obtain the appropriate current and voltage signals in the d-q axis. Finally, simulation and experimental results show that the proposed method not only achieves complete decoupling of the d-q axis currents, but also has good performance in terms of dynamic response, steady-state accuracy and robustness.

In order to overcome drawbacks of unreasonable cluster-head selection and excessive energy consumption in wireless sensor networks (WSNs), a modified cluster-head selection algorithm based on LEACH (LEACH-M) was proposed. Based on distributed address assignment mechanism (DAAM) of ZigBee, both residual energy and network address of nodes were taken into account to optimize cluster-head threshold equation. Furthermore, by leveraging a cluster-head competitive mechanism, LEACH-M successfully balanced the network energy burden and dramatically improved energy efficiency. The simulation results in NS-2.35 show that the proposed algorithm can prolong the network lifetime, minimize the energy consumption, and increase the amount of data received at base station whether region is in a 100 × 100m²or in a 300 × 300m².

Moving shadow elimination plays an important role in the field of moving object detection. However, the accuracy of shadow elimination is an open question, due to illumination and complex texture. Furthermore, the problem of misclassification of moving object caused by shadow has also become increasingly serious. To address this problem, this paper presents a moving shadow elimination algorithm based on the fusion of multi-feature pattern, which can enhance the accuracy of moving object detection system. In this approach, a dual-channel HSV color space feature and a uniform extended scale invariant local ternary pattern (UESILTP) texture feature are synthesized to elimination shadow. It greatly overcomes the misjudgment of dark object by color feature and the false detection caused by inconspicuous texture characteristics of moving object. Meantime, a method of region growth is adopted to fill the existing cavities in the color space. Finally, qualitative and quantitative comparisons with state-of-the-art methods show that the algorithm is effective.

To improve the reliability and robustness of the surface-mounted permanent magnet synchronous motor (SPMSM) system, a fast sliding-mode speed controller (FSMSC) combined with a nonlinear disturbance observer (NDOB) is proposed. First, the fast sliding-mode reaching law (FSMRL) is presented, which can quickly converge to the sliding-mode surface while maintaining a small chattering. Moreover, the stability of the FSMRL method is proved by the Lyapunov method. Second, an NDOB is introduced to observe the lumped disturbance of the system, which accurately estimates the disturbance and improves the robustness of the speed control system. Finally, the simulation and experimental results indicate that the proposed method can track the speed response and the change of the $d$ – $q$ -axis current with good performances, such as fast convergence, small chattering, and strong robustness.

Owing to their controlled mobility, extensive coverage and low cost, unmanned aerial vehicles (UAVs) have significant promise in the field of military, civilian and commercial applications, which has sparked a lot of research interest. This paper explores the application of the UAV in the symbiotic radio (SR) system. Specifically, we study the multi-user SR system assisted by the UAV and intelligent reflecting surface (IRS), and the modulation scheme with high flexibility is adopted to embed the IRS symbols into the UAV signals. First, we focus on minimizing the maximum bit error rate (BER) among all ground devices (GDs) while meeting the minimum communication rate requirements by jointly designing the active transmit beamforming and flight trajectory of the UAV as well as IRS reflective phase shifts. Then, we develop an iterative algorithm based on block coordinate descent (BCD) technology to address this optimization problem. Specifically, the optimization problem is decoupled into three sub-problems to be alternately solved, which are solved by invoking the semidefinite relaxation (SDR) and successive convex approximation (SCA) techniques. Moreover, the convergence performance and computational complexity of the proposed algorithm are analyzed. Finally, simulation results show the proposed algorithm can significantly improve the quality of IRS transmission.