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.

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.

Vehicular Edge Computing (VEC) is an effective paradigm in Internet of Vehicles, which allows vehicles to offload delay-sensitive tasks to nearby Road Side Units (RSUs) for processing, thereby enhancing the Quality of Service (QoS). However, the SDN controller that manages RSUs often have individual rationality and selfishness, and thus is unwilling to provide free computation resources to vehicles. Meanwhile, the dependency relationships among vehicular subtasks are not well investigated, resulting in unsatisfactory task latency and energy consumption. In order to effectively motivate the selfish SDN controller to participate in computation offloading and comprehensively consider all dependency situations among multiple subtasks, this paper proposes a Stackelberg game-based Dependency-aware task Offloading and resource Pricing framework (SDOP). Specifically, we first model the interaction between the SDN controller and vehicles as a Stackelberg game, where both parties wish to maximize their utility. Then, we employ the backward induction approach to analyze the investigated problem, and prove the existence and uniqueness of Nash and Stackelberg equilibrium. Next, we propose a Gradient Ascent Plus Genetic algorithm (GAPG) to solve the considered problem. Finally, extensive simulation results show that the proposed GAPG can significantly improve the utility of both the SDN controller and vehicles under various scenarios, when compared with other baseline schemes.