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The paper introduces the problems of encircling coverage and termination of a moving a deformable planar region by a mobile sensor/actuator network. We propose a decentralized randomized algorithm for self-deployment of a network of mobile robotic sensors/actuators for these problems. In the encircling coverage problem, the aim is to deploy sensors around a bounded connected region so that any point of a certain neighbourhood of the region is sensed by at least one mobile robotic sensor. In the termination problem, the aim is to terminate a moving region that may represent an oil spill or a hazardous chemical field. In this case, the moving robots are equipped with not only sensors but with actuators releasing neutralizing chemical so that the shape of the polluted region is controlled. The proposed algorithm is based only on information about the closest neighbours of each sensor. The moving region is of an arbitrary shape and not known to the sensors a priori. We give mathematically rigorous proofs of asymptotic optimality and convergence with probability 1 of the proposed randomized algorithm.

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Aerial refueling has demonstrated significant benefits to aviation by extending the range and endurance of aircraft [1]. The development of autonomous aerial refueling (AAR) techniques for unmanned aerial vehicles (UAVs) makes new missions and capabilities possible [2], like the ability for long range or long time flight. As the most widely used aerial refueling method, the probedrogue refueling (PDR) system is considered to be more flexible and compact than other refueling systems. However, a drawback of PDR is that the drogue is passive and susceptible to aerodynamic disturbances [3]. Therefore, it is difficult to design an AAR system to control the probe on the receiver to capture the moving drogue within centimeter level in the docking stage.It used to be thought that the aerodynamic disturbances in the aerial refueling mainly include the tanker vortex, wind gust, and atmospheric turbulence. According to NASA Autonomous Aerial Refueling Demonstration (AARD) project [2], the forebody flow field of the receiver may also significantly affect the docking control of AAR, which is called "the bow wave effect" [4]. As a result, the modeling and simulation methods for the bow wave effect were studied in our previous works [5,6].Since the obtained mathematical models are somewhat complex and there may be some uncertain factors in practice, this paper aims to use a model-free method to compensate for the docking error caused by aerodynamic disturbances including the bow wave effect.Most of the existing studies on AAR docking control do not consider the bow wave effect. In [7][8][9], the drogue is assumed to be relatively static (or oscillates around the equilibrium) and not affected by the flow field of the receiver forebody. However, in practice, the receiver aircraft is affected by aerodynamic disturbances, and the drogue is affected by both the wind disturbances and the receiver forebody bow wave. As a major difficulty in the control of AAR, the aerodynamic disturbances, especially the bow wave effect, attract increasing attention in these years. In [10,11], the wind effects from the tanker vortex, the wind gust, and the atmospheric turbulence are analyzed, and in [5,6,12], the modeling and simulation methods for the receiver forebody bow wave effect are studied, but no control methods are proposed. In [4], simulations show that the bow wave effect can be compensated by adding an offset value to the reference trajectory, but the method for obtaining the offset value is not given.

In this paper, a novel dynamic sliding mode control scheme is proposed for a class of uncertain stochastic nonlinear time-delay systems represented by Takagi-Sugeno fuzzy models. The key advantage of the proposed scheme is that two very restrictive assumptions in most existing sliding mode control approaches for stochastic fuzzy systems have been removed. It is shown that the closed-loop control system trajectories can be driven onto the sliding surface in finite time almost certainly. It is also shown that the stochastic stability of the resulting sliding motion can be guaranteed in terms of linear matrix inequalities; moreover, the sliding-mode controller can be obtained simultaneously. Simulation results illustrating the advantages and effectiveness of the proposed approaches are also provided.