Search

This article is concerned with the problem of autonomous configuration estimation for hyperscale distributed spacecraft systems in the case GNSS signals are not available or reliable. A decentralized control input estimation method based on the consensus protocol is proposed to reduce the uncertainty introduced by constellation configuration adjustment. According to Bayesian theory, we derive the double convolution of multivariate Gaussian distributions to fuse the results of autonomous input estimation. Combined with the covariance synchronization update technology, the design of an autonomous configuration estimation algorithm for hyperscale distributed spacecraft systems is implemented. Finally, the proposed algorithm is verified in a high-orbiting gravitational wave detector constellation with intersatellite laser interferometers.

This article addresses the adaptive fuzzy fault-tolerant output containment control of fully heterogeneous nonlinear multiagent systems subject to stochastic disturbances, where not only followers but also leaders are equipped with nonidentical dynamics. First, fully distributed fixed-time observers are designed to observe active leaders' information including system matrices, positions, and external inputs, which are only accessible to a limited group of followers. Subsequently, adaptive algorithms are utilized, informed by the observations of leaders' system matrices, to solve the regulator equations. Then, the state observer-based controller is proposed, which employs the adaptive fuzzy-based approximation law to approximate the unknown nonlinear item and integrates the adaptive gains to cope with the actuator faults in the follower's dynamics. In addition, a set of modest conditions and rigorous proofs is constructed to guarantee the accomplishment of predetermined output containment through the output regulation method and Lyapunov stability theory. Finally, the theoretical analysis is validated through performing a numerical simulation.

Accurate state of charge (SOC) is crucial to achieving safe, reliable, and efficient use of batteries. This article proposes an adaptive neural network (NN)-based event-triggered observer to estimate SOC. First, a stochastic battery equivalent circuit model (ECM) is established, where an adaptive NN is employed to approximate the unknown nonlinear part. The learning process of network weight is conducted online to observe the variations of model parameters and avoid time-consuming processes for parameter extraction. Besides, for the purpose of saving computational cost, an event-triggered mechanism (ETM) is employed in the weight updating law, which means the weights only update when it is necessary. Then, an adaptive radial basis function (RBF) NN-based SOC observer is designed, and its stability is proven by the Lyapunov theory. Moreover, the strictly positive lower bound of interevent time is derived, and undesirable Zeno behavior can be excluded. Finally, the accuracy and robustness of the proposed observer are evaluated by experiments and simulations. Results show that the proposed method can estimate SOC accurately in the presence of initial deviation and sensor noises.

In this article, we investigate a distributed constrained optimal formation matching problem for a large-scale multiagent system. A distributed formation matching algorithm for a large-scale multiagent system (DFMA-LSMAS) is proposed. The algorithm employs a distributed continuous-time strategy to deal with a minimal weight bipartite graph matching problem for the optimal matching relationship between each agent and each hole in the formation configuration. It prevents a centralized structure and the explosion of storage spaces compared with the Kuhn-Munkras algorithm. Additionally, DFMA-LSMAS utilizes a distributed parameter projection approach for the optimal location of the formation configuration subjected to a common state constraint. It reduces the growth of the auxiliary variables with the scale of the multiagent system. In the special case, an unmatched phenomenon appears which may cause the failure of DFMA-LSMAS. Therefore, a perturbation-based algorithm is provided to eliminate the influence of this phenomenon, but does not affect the optimality of the solution. Finally, simulation results are provided to verify the algorithms.

The $H_{\infty }$ output anti-windup(AW) consensus problem based on the reduced-order interval observers(ROIOs) for the nonlinear heterogeneous multi-agent systems (HMAS) with actuator constraints is studied in this brief. Firstly, the reduced order interval observers are derived to estimate the bounds of state for each agent with nonlinear dynamics. Then, a distributed output-feedback protocol is proposed to achieve the $H_{\infty }$ output anti-windup consensus in the presence of disturbance and unknown nonlinear function, which is associated with the interval observers, the anti-windup compensator and the leader-state estimation. The anti-windup compensator is designed for each agent by an extra internal state to alleviate degradation encountered with actuator saturation. Finally, the simulation results validate that the presented control scheme can address system stability despite existing uncertainties and actuator saturation.

This article studies the impact of constrained optimal stealthy attacks on the state estimator, where man-in-the-middle attacks with a linear form can compromise innovations transmitted through a wireless network. First, a novel resource-constrained attack model is proposed, in which there are only a finite number of attack instants within a fixed interval. Second, the evolution of the estimation error covariance under attacks is obtained, and the covariance at the ultimate instant of the attack interval is regarded as the attacker’s cost function. Moreover, a relaxed condition of the strict stealthiness, named Kullback–Leibler divergence, is employed to describe the attacker’s the stealthiness metric. Third, the one-time and holistic optimization problems of stealthy attacks are solved by exploiting the Lagrange multiplier method. Then the constrained optimal attack strategies are obtained to produce the largest ultimate estimation error covariance. Finally, two simulation cases are provided to confirm the correctness of the designed attack strategies.

This work proposes a memory fusion controller design methodology for sampled-data control of fractional-order (FO) systems with sliding memory window. Composed of finite-dimensional previous inputs, the devised controller is capable of handling hereditary effect and meanwhile enabling pseudo state to satisfy general integer-order (IO) discrete plant at sampling instants. Additionally, the asymptotical stability of controller and sampling error are further guaranteed. The developed fusion controller provides an “out-of-the-box” method for users who are not familiar with FO calculus and significantly facilitates the corresponding analysis. The above mentioned approach is thereafter employed in a more sophisticated case, that is, the coordination control of FO multiagent systems (MASs) subjects to intermittent sampled-data transmission. It is proved that the achievement of consensus only relates to the connectivity of communication graph. Numerical results are presented finally to substantiate the proposed control strategy.