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Quadrotors are making a difference to numerous civilian and military applications. There are various approaches to realize formation flight control. In this paper, a comparison between sliding mode and second-order consensus approaches for quadrotor leader-follower formation flight was conducted. Sliding mode and second-order consensus algorithms were employed to design the formation controller for steady formation flight. Both leader and the follower quadrotors are controlled by proportional integral derivative controllers. The leader follows the desired trajectory while the followers follow the instruction calculated by the formation controller. Simulations are carried out to analyze the effect of these two approaches. The result indicates that the followers can follow the leader's trajectory and keep the formation fairy well under the control of both approaches. However, the sliding mode approach can achieve faster position convergence effect, while the second-order consistency approach has better disturbance suppression performance in case of disturbance.

For traditional integrated navigation system based on SINS/GNSS, the integrated navigation system will work in standalone inertial navigation when GNSS signals are lost. In this situation, the divergence of SINS error is fast without GNNS information correction. Besides, the divergence of SINS error is related to the motion mode of the vehicle. This paper proposes a BP neural network (BPNN) aided integrated navigation method based on vehicle motion learning. The inputs of the neural network are 10-dimension, including the outputs of the IMU, the three attitude angles estimated by SINS, and the GNSS interruption lasting time. The outputs are three predictions of SINS position error. When GNSS is effective, the neural network is used to learn the SINS error divergence characteristics under several basic motion modes of the vehicle. When the GNSS signal is disturbed, the neural network is used to correct the position error of the SINS to improve the navigation accuracy. Finally, the effectiveness of the algorithm is verified by using an 8-shape motion of the vehicle.

Grid voltage faults may cause grid voltage phase changes in grid-connected modular multilevel converter (MMC) systems, leading to power factor degradation and excessive reactive power in grid-connected MMC systems. To see the effect of reactive power on the stability of an MMC system, in this paper, the effects of the grid voltage phase on the control loop and hence stability of the system are analyzed. The Lyapunov method is adopted to identify the critical grid phases in grid-connected MMCs. By comparing the effects of the various parts of the control loop, it is concluded that the unstable response of MMCs under phase change is caused by the instability of the active power loop. The analytical results are verified by simulations of a three-phase 21-level MMC.

The photovoltaic-battery hybrid power system (PBHPS) is a typical multi-converter interconnected system with two or more converter connected in series and parallel. When a large signal disturbance occurs, the stability of the PBHPS will be threatened due to the interaction between the source converters and the constant power load. We simplify the circuit of the PBHPS with multiple converters, and construct a large signal model with reduced order of the system. Furthermore, a large signal stability criterion is derived with mixed potential theory. The variation range of the parameters to ensure the stability is given. Finally, the validity of proposed criteria is verified by cycle-by-cycle simulations in a PBHPS of 1 kW.

Modular Multilevel Converter (MMC) has great application prospects for the flexible HVDC transmission, high-power motor drive and so on. In this paper, a discrete-time model of the MMC based on the virtual ergodic method is proposed for the typical MMC topology. First, the MMC topology is analyzed and its continuous-time model is established. Then, the state equation in the continuous-time model is discretized, and the inhomogeneous differential equation about the AC and DC current in MMC is solved by an iterative method. Thus, the discrete-time model of the MMC can be established. The model can not only describe the voltage and current characteristics of MMC mathematically, but also reflect the switching states of the sub-modules in detail. Finally, comparing the proposed mathematical model with the 21-level MMC simulation built in PSCAD, the effectiveness of the discrete-time model is verified.

The three-phase voltage source converter (VSC) suffers from complex dynamics due to large signal disturbances such as grid voltage dips. In this paper, the instability phenomena of current loop and voltage loop are identified in three-phase grid-connected VSCs. Moreover, the interaction of the voltage loop and current loop is analyzed. Thus, the fault current under such situation can be calculated by the analysis. The analysis results are verified in cycle-by-cycle simulations.

In the background of large data, information security faces more opportunities and challenges. Image steganography as an important means of information hiding, is widely used in military, medical, commercial and other important occasions. Two-dimensional code image as a convenient means of information exchange everywhere. In this paper, an improved LSB information hiding algorithm is designed based on the combination of image steganography and two-dimensional code. The two-dimensional code as a special image carrier, in which the RGB channel embeds secret information to carry out covert communication, has the advantages of simple implementation and high security. The experimental results show that the LSB embedding algorithm proposed in this paper has good invisibility, and the embedding capacity is very large, which is robust to noise and shear.

State estimation of a maneuvering target by bearing-only observations which suffer from set-membership and statistics uncertainties, an ellipsoidal set based filter is proposed in this paper. The influence of two uncertainties is expressed in terms of a set of probability distributions with an ellipsoidal set of means and a covariance matrix. We choose the generalization radius of ellipsoid as the optimality criterion to obtain “tightly” outer bounding ellipsoids. Through simulations of two-state maneuvering tracking problem with bearing-only maneuvering target tracking using two static platforms, proposed estimator has been compared with the extend set-membership filer and the extend Kalman filter. Results illustrate the effectiveness of the ellipsoidal set based filter.

This paper considers the newsvendor problem with two competing retailers who face price-sensitive and uncertain demand. There are two questions: (1) how to model the price-dependent stochastic demand and retail competition and (2) how to derive the combined pricing and inventory solutions. In order to answer above problems, we adopt multiplicative demand function and model the expected demand as an exponential function of both retailers' prices, then it is concluded that each retailer's best response price is unique and independent of the competitor's price decision, and each retailer's best response stocking factor is uniquely determined when the demand distribution satisfying the IFR property.

This paper studies coordination in a supply chain with one manufacture and one retailer using the flexible supply contracts via call options. First we derive the manufacture's optimal production decisions and the retailer's optimal order decisions. Then we obtain an contract set, in which any option contract can coordinate the supply chain. We also prove that using the option contracts in this set, the manufacture's profit, the retailer's profit and the supply chain's total profit can also increase, compared with the wholesale price contract.

Uncertainties inherent in customer demands make it difficult for supply chains to achieve just-in-time inventory replenishment, resulting in loosing sales opportunities or keeping excessive chain-wide inventories. In this paper, two adaptive inventory-control models, a centralized model and a decentralized one, are proposed for a supply chain consisting of one supplier and one retailers. The objective of the two models is to satisfy a target service level predefined for each retailer and to minimize the whole inventory cost. The inventory-control parameters of the supplier and retailers are safety lead time and safety stocks, respectively. Unlike most extant inventory-control approaches, modelling the uncertainty of customer demand as a statistical distribution is not a prerequisite in the two models. Instead, using a reinforcement learning technique called action-reward method, the control parameters are designed to adaptively change as customer demand patterns changes. A simulation-based experiment was performed to compare the performance of the two inventory control models.

With the economic globalization, networking, rapid development of IT and customization of customer requirements, supply chains must adaptively respond to its dynamic environment and customer requirements in order to keep long-term survival in a rigorous competition world. Traditional supply chains must change to adaptive supply networks (ASN). It is significant to study thoroughly the conception, characters and enabling technologies of ASN in current competitive market environment. ASNs have gained focus of both academic and practice areas. This paper gives a brief survey on ASN including definition, properties, research status and future trend.