Inspired by the integrated guidance and control design for endo-atmospheric aircraft, the integrated position and attitude control of spacecraft has attracted increasing attention and gradually induced a wide variety of study results in last over two decades, fully incorporating control requirements and actuator characteristics of space missions. This paper presents a novel and comprehensive survey to the coupled position and attitude motions of spacecraft from the perspective of dynamics and control. To this end, a systematic analysis is firstly conducted in details to show the position and attitude mutual couplings of spacecraft. Particularly, in terms of the time discrepancy between spacecraft position and attitude motions, space missions can be categorized into two types: space proximity operation and space orbital maneuver. Based on this classification, the studies on the coupled dynamic modeling and the integrated control design for position and attitude motions of spacecraft are sequentially summarized and analyzed. On the one hand, various coupled position and dynamic formulations of spacecraft based on various mathematical tools are reviewed and compared from five aspects, including mission applicability, modeling simplicity, physical clearance, information matching and expansibility. On the other hand, the development of the integrated position and attitude control of spacecraft is analyzed for two space missions, and especially, five distinctive development trends are captured for space operation missions. Finally, insightful prospects on future development of the integrated position and attitude control technology of spacecraft are proposed, pointing out current primary technical issues and possible feasible solutions.

This paper addresses distributed adaptive optimal resource allocation problems over weight-balanced digraphs. By leveraging state-of-the-art adaptive coupling designs for multiagent systems, two adaptive algorithms are proposed, namely a directed-spanning-tree-based algorithm and a node-based algorithm. The benefits of these algorithms are that they require neither sufficiently small or unitary step sizes, nor global knowledge of Laplacian eigenvalues, which are widely required in the literature. It is shown that both algorithms belong to a class of uncertain saddle-point dynamics, which can be tackled by repeatedly adopting the Peter-Paul inequality in the framework of Lyapunov theory. Thanks to this new viewpoint, global asymptotic convergence of both algorithms can be proven in a unified way. The effectiveness of the proposed algorithms is validated through numerical simulations and case studies in IEEE 30-bus and 118-bus power systems.

In the era of big data, there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data. Data security and data pricing, however, are still widely regarded as major challenges in this respect, which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing mechanisms. In this context, data recording and trading are conducted separately within two separate blockchains: the data blockchain (DChain) and the value blockchain (VChain). This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary market. Moreover, pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and users. Specifically, in regular data trading on VChain-S2D, two auction models are employed according to the demand scale, for dealing with users’ strategic bidding. The incentive-compatible Vickrey-Clarke-Groves (VCG) model is deployed to the low-demand trading scenario, while the nearly incentive-compatible monopolistic price (MP) model is utilized for the high-demand trading scenario. With temporary data trading on VChain-D2S, a reverse auction mechanism namely two-stage obscure selection (TSOS) is designed to regulate both suppliers’ quoting and users’ valuation strategies. Furthermore, experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

Goal-conditioned reinforcement learning (RL) is an interesting extension of the traditional RL framework, where the dynamic environment and reward sparsity can cause conventional learning algorithms to fail. Reward shaping is a practical approach to improving sample efficiency by embedding human domain knowledge into the learning process. Existing reward shaping methods for goal-conditioned RL are typically built on distance metrics with a linear and isotropic distribution, which may fail to provide sufficient information about the ever-changing environment with high complexity. This paper proposes a novel magnetic field-based reward shaping (MFRS) method for goal-conditioned RL tasks with dynamic target and obstacles. Inspired by the physical properties of magnets, we consider the target and obstacles as permanent magnets and establish the reward function according to the intensity values of the magnetic field generated by these magnets. The nonlinear and anisotropic distribution of the magnetic field intensity can provide more accessible and conducive information about the optimization landscape, thus introducing a more sophisticated magnetic reward compared to the distance-based setting. Further, we transform our magnetic reward to the form of potential-based reward shaping by learning a secondary potential function concurrently to ensure the optimal policy invariance of our method. Experiments results in both simulated and real-world robotic manipulation tasks demonstrate that MFRS outperforms relevant existing methods and effectively improves the sample efficiency of RL algorithms in goal-conditioned tasks with various dynamics of the target and obstacles.

This paper presents a subspace identification method for closed-loop systems with unknown deterministic disturbances. To deal with the unknown deterministic disturbances, two strategies are implemented to construct the row space that can be used to approximately represent the unknown deterministic disturbances using the trigonometric functions or Bernstein polynomials depending on whether the disturbance frequencies are known. For closed-loop identification, CCF-N4SID is extended to the case with unknown deterministic disturbances using the oblique projection. In addition, a proper Bernstein polynomial order can be determined using the Akaike information criterion (AIC) or the Bayesian information criterion (BIC). Numerical simulation results demonstrate the effectiveness of the proposed identification method for both periodic and aperiodic deterministic disturbances.

The secure dominating set (SDS), a variant of the dominating set, is an important combinatorial structure used in wireless networks. In this paper, we apply algorithmic game theory to study the minimum secure dominating set (MinSDS) problem in a multi-agent system. We design a game framework for SDS and show that every Nash equilibrium (NE) is a minimal SDS, which is also a Pareto-optimal solution. We prove that the proposed game is an exact potential game, and thus NE exists, and design a polynomial-time distributed local algorithm which converges to an NE in O (n) rounds of interactions. Extensive experiments are done to test the performance of our algorithm, and some interesting phenomena are witnessed.

Most existing domain adaptation (DA) methods aim to explore favorable performance under complicated environments by sampling. However, there are three unsolved problems that limit their efficiencies: i) they adopt global sampling but neglect to exploit global and local sampling simultaneously; ii) they either transfer knowledge from a global perspective or a local perspective, while overlooking transmission of confident knowledge from both perspectives; and iii) they apply repeated sampling during iteration, which takes a lot of time. To address these problems, knowledge transfer learning via dual density sampling (KTL-DDS) is proposed in this study, which consists of three parts: i) Dual density sampling (DDS) that jointly leverages two sampling methods associated with different views, i.e., global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information; ii) Consistent maximum mean discrepancy (CMMD) that reduces intra- and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS; and iii) Knowledge dissemination (KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target domain. Mathematical analyses show that DDS avoids repeated sampling during the iteration. With the above three actions, confident knowledge with both global and local properties is transferred, and the memory and running time are greatly reduced. In addition, a general framework named dual density sampling approximation (DDSA) is extended, which can be easily applied to other DA algorithms. Extensive experiments on five datasets in clean, label corruption (LC), feature missing (FM), and LC&FM environments demonstrate the encouraging performance of KTL-DDS.

In this paper, the networked control problem under event-triggered schemes is considered for a class of continuous-time linear systems with random impulses. In order to save communication costs and lighten communication burden, a dynamic event-triggered scheme whose threshold parameter is dynamically adjusted by a given evolutionary rule, is employed to manage the transmission of data packets. Moreover, the evolution of the threshold parameter only depends on the sampled measurement output, and hence eliminates the influence of impulsive signals on the event-triggered mechanism. Then, with the help of a stochastic analysis method and Lyapunov theory, the existence conditions of desired controller gains are received to guarantee the corresponding input-to-state stability of the addressed system. Furthermore, according to the semi-definite programming property, the desired controller gains are calculated by resorting to the solution of three linear matrix inequalities. In the end, the feasibility and validity of the developed control strategy are verified by a simulation example.