In this paper, a novel particle swarm optimization (PSO) algorithm is put forward where a sigmoid-function-based weighting strategy is developed to adaptively adjust the acceleration coefficients. The newly proposed adaptive weighting strategy takes into account both the distances from the particle to the global best position and from the particle to its personal best position, thereby having the distinguishing feature of enhancing the convergence rate. Inspired by the activation function of neural networks, the new strategy is employed to update the acceleration coefficients by using the sigmoid function. The search capability of the developed adaptive weighting PSO (AWPSO) algorithm is comprehensively evaluated via eight well-known benchmark functions including both the unimodal and multimodal cases. The experimental results demonstrate that the designed AWPSO algorithm substantially improves the convergence rate of the particle swarm optimizer and also outperforms some currently popular PSO algorithms.

In this brief, attitude control is investigated for a quadrotor under gust wind via a dual closed-loop control framework. In the dual closed-loop framework, active disturbance rejection control and proportional-derivative control are used in the inner and outer loops, respectively. The perturbations of gust wind are considered as dynamic disturbances, which are estimated by an extended state observer in the inner loop. Both convergence and stabilization are given for the extended state observer and the closed-loop system, respectively. Experimental results are given to show the effectiveness of the proposed method for quadrotors.

In this paper, the state estimation problem is investigated for a class of continuous-time complex networks with bounded distributed delay. For the network under consideration, only the outputs from a fraction of nodes are available and this put forward the new challenge, that is, the so-called partial-nodes-based state estimation problem. In order to reduce the usage of the communication resources, a general event-triggering rule is considered in the design of the estimator. A novel estimator is constructed and, by constructing novel Lyapunov-Krasovskii functionals, some easy-to-check conditions are derived such that the error dynamics is exponentially ultimately bounded. Furthermore, it is shown that the Zeno behavior can be excluded from the event-triggering rules. Numerical simulations are presented to further illustrate the effectiveness of the theoretical results.

This paper is concerned with the problem of distributed H∞ filtering for switched stochastic time-delay systems with fading measurements over sensor networks. The underlying target plants are subject to fading measurements where the fading rates are described by continuous-time random variables with known statistical properties dependent on the system modes. The adjacency matrices characterizing the topology of the sensor networks are also allowed to be mode-dependent. Based on the multiple Lyapunov functional approach and average dwell-time concept, the distributed H _∞ filter is designed by means of the convex optimization scheme. A dedicated technique is developed via a simple algebraic equality in order to avoid solving a transcendental equation used in the existing results. With the designed filter, the error dynamics of the state estimation is guaranteed to have the mean-square exponential stability with a prescribed H _∞ disturbance attenuation level. Finally, a numerical example is used to demonstrate the effectiveness of the method.

One of the key problems for space vehicles is how to deal with the contradiction between the control constraints and the disturbances from multiple resources. This paper focuses on the adaptive full state constrained controller design problem for the flexible air-breathing hypersonic vehicles with multisource uncertainties. In simultaneous presence of the aerodynamical uncertainties, the modeling errors, the external disturbances, and the contingent actuator failures, an adaptive fault-tolerant control scheme is put forward in the context of dynamic surface control. Then, the barrier Lyapunov functions are utilized to guarantee that the constraints on the velocity, the flight path angle, the altitude, and the pitch rate are never violated. In virtue of the bound estimation approach, the multisource disturbances are effectively dealt with. Finally, a number of illustrative examples are provided to demonstrate the effectiveness of the proposed methodology.

In this brief, the new problem of partial-nodes-based (PNB) state estimation problem is investigated for a class of complex network with unbounded distributed delays and energy-bounded measurement noises. The main novelty lies in that the states of the complex network are estimated through measurement outputs of a fraction of the network nodes . Such fraction of the nodes is determined by either the practical availability or the computational necessity. The PNB state estimator is designed such that the error dynamics of the network state estimation is exponentially ultimately bounded in the presence of measurement errors. Sufficient conditions are established to ensure the existence of the PNB state estimators and then the explicit expression of the gain matrices of such estimators is characterized. When the network measurements are free of noises, the main results specialize to the case of exponential stability for error dynamics. Numerical examples are presented to verify the theoretical results.