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This article considers both the influence of relay power and load control period on the load tracking performance in smart grid communication. First, based on regulation errors caused by load control with imperfect channel state information (CSI), the load tracking cost model integrated of load control period and relay power is established in smart grid communication. Then, a coordination mechanism of cost-sharing contract (CSC) is presented. In the CSC, the utility companies select the preferred contractual terms offered by the telecom operator (TO) to reduce their costs and coordinate the whole network system simultaneously. Finally, the theoretical analysis and simulation demonstrate that the simultaneous consideration of the relay power and load control period can reduce the costs of the utility companies, increase the profit of the TO, and improve the social welfare. Besides, the proposed coordination mechanism of CSC can coordinate the whole network system.

In this article, we study the tracking problem for networked nonlinear discrete systems with actuator faults and dual-direction data dropouts. A novel adaptive fault-tolerant iterative learning model-free control strategy is designed. First, by utilizing the method called compact form dynamic linearization, the original nonlinear system model is transformed into an equivalent data-driven model, and the data model contains only one unknown parameter. Both the actuator fault and the system dynamics information are included in this parameter. Then, to model the physical processes of data dropout, a new mathematical relationship is constructed. Furthermore, an adaptive fault-tolerant iterative learning tracking control scheme is developed with only randomly received input/output data. Noting that the high learning rate or convergence rate is required in actual applications, a new varying parameter approach is designed to improve such rate. Finally, it is rigorously proved that the closed loop is stable in the sense of uniform ultimate boundedness, and numerical simulation results are conducted to validate the effectiveness of the designed control strategy.

In this paper, the adaptive prescribed performance tracking control of nonlinear asymmetric input saturated systems in strict-feedback form is addressed under the consideration of model uncertainties and external disturbances. A radial basis function neural network (RBF-NN) is utilized to handle the model uncertainties. By prescribed performance functions, the transient performance of the system can be guaranteed. The continuous Gaussian error function is represented as an approximation of asymmetric saturation nonlinearity such that the backstepping technique can be leveraged in the control design. Based on the Lyapunov synthesis, residual function approximation inaccuracies and external disturbances are compensated by constructed adaptive control laws. As a consequence, all the signals in the closed-loop system are uniformly ultimately bounded and the tracking errors bounded by prescribed functions converge to a small neighbourhood of zero. The proposed method is applied to the autonomous underwater vehicles (AUVs) with extensive simulation results demonstrating the effectiveness of the proposed method.

Tracking an autonomous underwater vehicle (AUV) has been regarded as one of the most key applications for Internet of Underwater Things (IoUT). However, the strong mobility of AUV as well as asynchronous clock, stratification effect, and high energy consumption of acoustic communication make it challenging to achieve such a task. To handle the above issues, this article develops a ubiquitous tracking scheme for AUV. The tracking scheme is divided into two stages, i.e.: 1) motion prediction and 2) persistent tracking. In the first stage, an unscented transform-based localization estimator is utilized by sensor nodes to acquire the initial position of AUV, through which a terminal sliding-mode velocity observer is designed to predict the mobility trajectory of AUV. With the predicted mobility trajectory, a minimum rigid-graph-based tracking strategy is developed in the second stage to enable ubiquitously tracking. For the designed tracking strategy, the posterior Cramer–Rao lower bound is selected as the benchmark to optimize the network topology, such that a minimum rigid graph can be generated to balance the tradeoff between tracking accuracy and energy consumption. Particularly, the duty-cycle mechanism and the unscented Kalman filtering are jointly adopted to prolong the network lifetime and improve the tracking accuracy. Finally, simulation and experimental results are presented to show the effectiveness of our approach.

Privacy-preserving localization for Internet of Underwater Things (IoUT) plays a fundamental role in the sensing, communication and control of ocean environments. However, the unique characteristics of underwater environment make it much more difficult to achieve such a task. In this article, we are concerned with a privacy-preserving localization issue for IoUT, subjected to asynchronous clock, stratification effect and forging attack in cyber channels. In order to eliminate the influence of asynchronous clock and hide the private position information, we develop a privacy-preserving asynchronous transmission protocol, where a received signal strength (RSS)-based detection strategy is given to detect the malicious anchor nodes. Based on this, a least squares estimator is designed to estimate the position information of target. Particularly, a ray compensation strategy is incorporated into the localization estimator, such that the localization bias from assuming the straight-line transmission can be avoided. It is worth mentioning that, the proposed localization solution in this article can not only hide the private position information, but also eliminate the influences of asynchronous clock, stratification effect and forging attack. Finally, simulation and experiment results are conducted to reveal that the proposed localization solution outperforms the other existing works in terms of localization accuracy and effectiveness.

With the global awareness of climate change and environmental problems, major powers over the world have set their goals to contribute to a low-carbon society. China announced the target of "carbon peak and carbon neutrality," requiring a cleaner, carbonfree, economic, and sustainable energy system. To this end, there are two promising approaches. The first is to increase the penetration of clean energies, such as wind power and photovoltaics (PVs). This can reduce carbon emissions effectively. In recent years, China has seen an upsurge in renewable energies, where the installed capacity of new energy generation is more than 388 GW, ranked first in the world for many years. The second approach is to develop integrated energy systems (IESs), which consist of multiple energy carriers, such as fuel cells, electric boilers, combined cooling, heating, and power plants, wind generators, and PVs and energy storage devices. IESs can leverage synergetic effects across multiple energy carriers, such as cascaded utilization of waste heat during electricity generation, and thus achieve higher overall efficiency. An IES is sometimes called the Energy Internet in China and has gained increasing attention during the past few years. In short, the traditional power system is undergoing a transition to IESs with a high penetration of renewable energies.

In industrial wireless sensor networks (IWSNs), a lot of energy is wasted in the form of electromagnetic radiations. It can be effectively utilized with energy harvesting (EH), which absorbs part of the energy in the transmission signal but reduces the throughput and reliability of IWSNs. In this article, we study the throughput optimization of IWSNs with EH from the interference radio-frequency (RF) signal considering the reliability constraint of the industrial information transmission. Under the premise of limited energy supply of EH relays, the throughput maximization of IWSNs is formulated as a nonconvex optimization problem. In order to transform the nonconvex problem to a convex optimization problem, the successive convex approximation (SCA) approach is adopted. Furthermore, a power allocation algorithm is designed to maximize the total transmission rate of the network. Simulation results demonstrate that the proposed algorithm can maximize the throughput under the primise of SINR reliability.

Traditionally, offline optimization of power systems is acceptable due to the largely predictable loads and reliable generation. The increasing penetration of fluctuating renewable generation and Internet-of-Things devices allowing for fine-grained controllability of loads have led to the diminishing applicability of offline optimization in the power systems domain, and have redirected attention to online optimization methods. However, online optimization is a broad topic that can be applied in and motivated by different settings, operated on different time scales, and built on different theoretical foundations. This paper reviews the various types of online optimization techniques used in the power systems domain and aims to make clear the distinction between the most common techniques used. In particular, we introduce and compare four distinct techniques used covering the breadth of online optimization techniques used in the power systems domain, i.e., optimization-guided dynamic control, feedback optimization for single-period problems, Lyapunov-based optimization, and online convex optimization techniques for multi-period problems. Lastly, we recommend some potential future directions for online optimization in the power systems domain.