This paper delves into the constant voltage output regulation problem for delayed inductive power transfer (IPT) systems with linear matrix inequality region constraints. Different from existing works, the proposed approach not only provides a model-free controller design procedure, but also deals with time-varying delays and packet losses. The main contributions are twofold. First, we propose a novel data-based parameterization of delayed systems. Second, data-based sufficient conditions are derived under which the delayed stability and LMI region constraints are guaranteed and then controller is parameterized. Finally, an experimental example is proposed to demonstrate the accuracy of proposed methods and the robustness of the suggested controllers.

This paper concerns the problem of constant voltage regulation for an unknown inductive power transfer (IPT) system with phase shift modulation (PSM). Different from mainstream model-based approaches, the proposed data-driven fuzzy method not only formulates a controller solely based on input-output data, but also guarantees the stability of nonlinear inductive power transfer systems. First, a data-based closed-loop fuzzy representation is derived to parameterize the nonlinear system. Subsequently, a sufficient data-based condition is proposed to ensure the stability of the nonlinear system and the asymptotically tracking characteristic without identifying the system parameters. Finally, an experimental example is proposed to demonstrate the accuracy of the proposed method and the robustness of suggested controllers.

The inductive power transfer (IPT) technology, as one of the main wireless power transfer methods, has been prevalent in both industrial and commercial applications. However, the circuit parameter perturbations and communication delays will deteriorate the transfer performance, including output fluctuation and efficiency drop. To deal with the above mentioned issues and maximize the transfer efficiency, a boost converter is first cascaded after series-series IPT topology, then a novel control strategy is proposed to fulfill the constant power output with maximum efficiency tracking (MET). In the proposed strategy, the MET condition can be derived regardless of coupling coefficient and load resistance through elaborate design. Next, the stability of nonlinear IPT systems subjected to communication delays is modeled and described based on the concept of Takagi-Sugeno (T-S) fuzzy models. Furthermore, an event-triggered$\bm{H_{\infty}}$fuzzy controller is designed to improve the robustness (against perturbations of coupling coefficient, loads and communication delay), and reduce the computation and transmission burden. Finally, the effectiveness of the suggested strategy is shown using the simulation results.Note to Practitioners—Maintaining a constant output power and high transmission efficiency remains a challenging task in IPT technology due to parameter variations, communication delays and model nonlinearity, especially in the charging scenarios of drones and electric vehicles. Therefore, an event-triggered$\bm{H_\infty}$fuzzy controller is proposed to cope with aforementioned situations such that the constant output power with high efficiency is guaranteed. Even in the presence of communication delays, the proposed strategy can still maintain stable output with high efficiency. In addition, the proposed method is equipped with the event-triggered mechanism to reduce the frequency of controller updates and transmission burden while maintaining the transient performance. The outcome of this paper provides a simple but useful MET condition and controller design procedure that can be employed to maintain the reliable and stable operation of IPT systems and optimize their transfer efficiency.

Movable antenna (MA) technology is highly promising for improving communication performance, due to its advantage of flexibly adjusting positions of antennas to reconfigure channel conditions. In this paper, we investigate MAs-assisted secure transmission under a legitimate transmitter Alice, a legitimate receiver Bob and multiple eavesdroppers. Specifically, we consider a practical scenario where Alice has no any knowledge about the instantaneous non-line-of-sight component of the wiretap channel. Under this setup, we evaluate the secrecy performance by adopting the secrecy outage probability metric, the tight approximation of which is first derived by interpreting the Rician fading as a special case of Nakagami fading and concurrently exploiting the Laguerre series approximation. Then, we minimize the secrecy outage probability by jointly optimizing the transmit beamforming and positions of antennas at Alice. However, the problem is highly non-convex because the objective includes the complex incomplete gamma function. To tackle this challenge, we, for the first time, effectively approximate the inverse of the incomplete gamma function as a simple linear model. Based on this approximation, we arrive at a simplified problem with a clear structure, which can be solved via the developed alternating projected gradient ascent (APGA) algorithm. Considering the high complexity of the APGA, we further design another scheme where the zero-forcing based beamforming is adopted by Alice, and then we transform the problem into minimizing a simple function which is only related to positions of antennas at Alice. Such problem is well-solved via another projected gradient descent algorithm developed with a lower complexity. As demonstrated by simulations, our proposed schemes achieve significant performance gains compared to conventional schemes based on fixed-position antennas.

Unmanned aerial vehicle (UAV) plays an important role in wireless communication systems, due to the additional degree of freedom realized from its flexible deployment. Driven by this advantage and considering the security issue, this paper aims to investigate UAV-enabled proactive eavesdropping over distributed transmit beamforming-based suspicious communications. Specifically, for the suspicious system, there are multiple suspicious clusters aiming to communicate with the suspicious destination (D) using mutually orthogonal frequency bands, and distributed transmit beamforming is exploited by each cluster to strengthen the signal receiving quality at D. For the legitimate party, the full-duplex UAV exploits one antenna to jam D and uses the other antenna to overhear the signals of the suspicious clusters concurrently. By resorting to the Laguerre series approximation and the central limit theorem, we first characterize, in closed form, the approximated distributions of the receiving signal-to-interference-noise ratio (SINR) at D and the UAV, which are shown to be very tight. Based on this analysis and considering that the suspicious system works in the delay-limited transmission mode or the delay-sensitive transmission mode, we aim to maximize the minimum eavesdropping success probability of the UAV for those suspicious communications links, by jointly adjusting the UAV’s deployment and jamming power allocations over different frequency bands. The problem is highly non-convex. To tackle this, we develop an alternative optimization framework and further a novel and low-complexity solution in the high SNR regime to the optimization problem. Simulation results show the effectiveness of our proposed schemes compared to competitive benchmarks.

In wireless communication systems, relay can improve the communication quality and increase communication distance. However, most of the current researches treat the relay node as a selfless node, and seldom pay attention to the individual needs and fairness. To address this issue, in this paper, we study power allocation scheme based on Game theory in untrusted relay networks. The model price incentive mechanism based on Stackelberg Game is used to solve the power allocation problem, which aims to achieve active participation assistance of relay and reduce the system signaling overhead. Meanwhile, the convergence of the algorithm is also analyzed. The simulation results show that compared with the existing fixed allocation methods, the power allocation based on this scheme has better destination node utility and global maximum secrecy rate. Moreover, we find that the dynamic power allocation strategy based on Stackelberg Game scheme is more suitable for dynamic scenes, which only rely on the imperfect channel state information.

Prediction on electricity load time series of commercial building and construction sets plays an important role in smart grid applications, building operation and environmental protection, etc. It differs significantly from the traditional aggregated electricity time series prediction problem and aims to provide more accurate prediction for each building respectively. In recent years, deep learning shows significant improvements on time series prediction over classical methods. However, it suffers from the problem of limited data. The problem is more serious in the context of electricity time series prediction of buildings. For instance, new buildings or buildings with newly installed meters have limited historical data, which results in poor model training and forecasts. In this paper we propose a novel generative adversarial learning based prediction method for dealing with this problem. We employ the generative adversarial network framework to obtain adversarially generated data for prediction model training by using long short-term memory units based recurrent neural network and attention mechanism. Extensive experiments on real-life dataset demonstrate the improvements of our proposed method against the traditional methods.