In this paper, we discuss the speed regulation problem of permanent magnet synchronous motor (PMSM) servo systems. Firstly, a continuous terminal sliding mode control (CTSMC) method is introduced for speed loops to eliminate the chattering phenomenon while still ensuring a strong disturbance rejection ability for the closed-loop system. However, in the presence of strong disturbances, the CTSMC law still needs to select high gain which may result in large steady-state speed fluctuations for the PMSM control system. To this end, an extended state observer (ESO)-based continuous terminal sliding mode control method is proposed. The ESO is employed to estimate system disturbances and the estimation is employed by the speed controller as a feed-forward compensation for disturbances. Compared to the conventional sliding mode control method, the proposed composite sliding control method obtains a faster convergence and better tracking performance. Also, by feed-forward compensating system disturbances and tuning down the gain of the CTSMC law, the fluctuation of steady-state speed of the closed-loop system is reduced while the disturbance rejection capability of the PMSM system is still maintained. Simulation and experimental results are provided to demonstrate the superior properties of the proposed control method.

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This article explores the speed regulation problem of permanent magnet synchronous motor (PMSM) systems subjected to unknown time-varying disturbances. A continuous sliding mode control (CSMC) technique is introduced for the speed loop to enhance the robustness of PMSM systems and eliminate the chattering phenomenon caused by high-frequency switch function in the conventional control law. However, the high control gain of the CSMC law in the presence of strong disturbances leads to large steady-state speed fluctuations for PMSM systems. In many application fields, PMSM systems are affected by time-varying disturbances instead of constant disturbances. For example, electric bicycles are usually affected by changing environmental disturbances, including wind speeds, road conditions, etc. These disturbances may be in the form of constant, ramp, and parabolic disturbances. Hence, a generalized proportional integral (GPI) observer is employed to estimate these types of disturbances. Then, the disturbance estimation method and the aforementioned CSMC method are combined to establish a composite sliding mode control method called the CSMC+GPI method for the speed loop of PMSM systems. Contrary to the conventional sliding mode control technique, the proposed method completely eliminates the chattering phenomenon caused by the switching function in the conventional control law. Moreover, a small control gain for the CSMC+GPI method is chosen by feed-forwarding estimated values to the speed controller. Hence, the steady-state speed fluctuations are small. The effectiveness of the proposed control scheme is verified by simulation and experimental result.

In this paper, we explore the technology of tracking a group of targets with correlated motions in a wireless sensor network. Since a group of targets moves collectively and is restricted within a limited region, it is not worth consuming scarce resources of sensors in computing the trajectory of each single target. Hence, in this paper, the problem is modeled as tracking a geographical continuous region covered by all targets. A tracking algorithm is proposed to estimate the region covered by the target group in each sampling period. Based on the locations of sensors and the azimuthal angle of arrival (AOA) information, the estimated region covering all the group members is obtained. Algorithm analysis provides the fundamental limits to the accuracy of localizing a target group. Simulation results show that the proposed algorithm is superior to the existing hull algorithm due to the reduction in estimation error, which is between 10% and 40% of the hull algorithm, with a similar density of sensors. And when the density of sensors increases, the localization accuracy of the proposed algorithm improves dramatically.

Small river reservoirs are widespread and can be ecologically sensitive across the dry-wet transition under monsoon climate with respect to nutrient loading and phenology. Monthly sampling and high-frequency in situ measurements were conducted for a river reservoir (southeast China) in 2013-2014 to examine the seasonal pattern of nutrients and phytoplankton. We found that nutrient concentrations were runoff-mediated and determined by watershed inputs and, in some cases, by internal cycling depending on hydrology and temperature. Ammonium and phosphate were relatively enriched in February-March (a transitional period from dry/cold to wet/hot climate), which can be ascribed to initial flushing runoff from human/animal waste and spring fertilizer use. A phytoplankton bloom (mainly Chlorophyta) occurred during April after a surge of water temperature, probably due to the higher availability of inorganic nutrients and sunlight and suitable hydraulic residence time (medium flow) in the transitional period. The concentration of phytoplankton was low during May-June (wet-hot climate) when the concentrations of total suspended matter (TSM) were highest, likely owing to the "shading" effect of TSM and turbulence of high flow conditions. Nutrient-algae shifts across the dry-wet season and vertical profiles suggested that algal blooms seem to be fueled primarily by phosphate and ammonium rather than nitrate. Current findings of a strong temporal pattern and the relationship between physical parameters, nutrient and biota would improve our understanding of drivers of change in water quality and ecosystem functions with dam construction.