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Robust constrained formation tracking control of underactuated underwater vehicles (UUVs) fleet in 3-D space is a challenging but practical problem. To address this problem, this article develops a novel consensus-based optimal coordination protocol and a robust controller, which adopts a hierarchical architecture. On the top layer, the spherical coordinate transform is introduced to tackle the nonholonomic constraint, and then a distributed optimal motion coordination strategy is developed. As a result, the optimal formation tracking of UUVs fleet can be achieved, and the constraints are fulfilled. To realize the generated optimal commands better and, meanwhile, deal with the underactuation, at the lower-level control loop a neurodynamics-based robust backstepping controller is designed, and in particular, the issue of "explosion of terms" appearing in conventional backstepping-based controllers is avoided and control activities are improved. The stability of the overall UUVs formation system is established to ensure that all the states of the UUVs are uniformly ultimately bounded in the presence of unknown disturbances. Finally, extensive simulation comparisons are made to illustrate the superiority and effectiveness of the derived optimal formation tracking protocol.

This article addresses distributed robust learning-based control for consensus formation tracking of multiple underwater vessels, in which the system parameters of the marine vessels are assumed to be entirely unknown and subject to the modeling mismatch, oceanic disturbances, and noises. Toward this end, graph theory is used to allow us to synthesize the distributed controller with a stability guarantee. Due to the fact that the parameter uncertainties only arise in the vessels' dynamic model, the backstepping control technique is then employed. Subsequently, to overcome the difficulties in handling time-varying and unknown systems, an online learning procedure is developed in the proposed distributed formation control protocol. Moreover, modeling errors, environmental disturbances, and measurement noises are considered and tackled by introducing a neurodynamics model in the controller design to obtain a robust solution. Then, the stability analysis of the overall closed-loop system under the proposed scheme is provided to ensure the robust adaptive performance at the theoretical level. Finally, extensive simulation experiments are conducted to further verify the efficacy of the presented distributed control protocol.

In this article, an extended state observer (ESO) design problem is investigated for uncertain nonlinear systems subject to limited network bandwidth. First, for rational information exchange scheduling, a dynamic event-triggered (DET) communication protocol is proposed. Different from the traditional static event-triggered strategies with fixed thresholds, an internal dynamic variable is introduced to be adaptively adjusted by a dual-directional regulating mechanism. Thus, more desirable tradeoff between observation performance and communication resource efficiency is achieved. Second, inspired by our early work on Takagi-Sugeno fuzzy ESO (TSFESO), a novel paradigm of event-triggered TSFESO is initially proposed. Third, under the DET mechanism, the TSFESO design approach is derived to carry out exponential convergence for estimation error dynamics. Finally, the effectiveness of the proposed method is verified by numerical examples. The nonlinear estimating efficiency and linear numerical tractability are integrated in TSFESO. In addition, a generalized ESO formulation is developed to allow some nonadditive uncertainties incompatible with total disturbance, such as improved event-triggered strategy, and thus, the application sphere of ESO is further expanded.

Motion control is critical in mobile robot systems, which determines the reliability and accuracy of a robot. Due to model uncertainties and widespread external disturbances, a simple control strategy cannot match tracking accuracy with disturbance immunity, while a complex controller will consume excessive energy. For precise motion control with disturbance immunity and low energy consumption, a control method based on an enhanced reduced-order extended state observer (ERESOBC) is proposed to control the motor-wheels dynamic model of a differential driven mobile robot (DDMR). In this method, only unknown state error and negative disturbance are estimated by the enhanced reduced-order extended state observer (ERESO), which reduces the required energy of the observer. In addition, a simple state-feedback-feedforward controller is used to track the reference signal and compensate for negative disturbance. Through numerical simulation and application example, the tracking performance and disturbance rejection performance of DDMR are compared with the traditional control method based on enhanced extended state observer (EESOBC), and the results show the superiority of the ERESOBC method.

In this article, a novel thruster information fusion fault diagnosis method for the deep-sea human occupied vehicle (HOV) is proposed. A deep belief network (DBN) is introduced into the multisensor information fusion model to identify uncertain and unknown, continuously changing fault patterns of the deep-sea HOV thruster. Inputs for the DBN information fusion fault diagnosis model are the control voltage, feedback current, and rotational speed of the deep-sea HOV thruster; and the output is the corresponding fault degree parameter ( s ), which indicates the pattern and degree of the thruster fault. In order to illustrate the effectiveness of the proposed fault diagnosis method, a pool experiment under different simulated fault cases is conducted in this study. The experimental results have proved that the DBN information fusion fault diagnosis method can not only diagnose the continuously changing, uncertain, and unknown thruster fault but also has higher identification accuracy than the information fusion fault diagnosis methods based on traditional artificial neural networks.

One-way-broadcast-based flooding time synchronization algorithms are commonly used in wireless-sensor networks (WSNs). However, the packet delay and clock drift pose a challenge to accuracy, as they entail serious by-hop error accumulation problems in the WSNs. To overcome this, a rapid-flooding multibroadcast time synchronization with real-time delay compensation (RDC-RMTS) is proposed in this article. By using a rapid-flooding protocol, flooding latency of the referenced time information is significantly reduced in the RDC-RMTS. In addition, a new joint clock skew-offset maximum-likelihood estimation (MLE) is developed to obtain the accurate clock parameter estimations and the real-time packet delay estimation. Moreover, an innovative implementation of the RDC-RMTS is designed with an adaptive clock offset estimation. The experimental results indicate that the RDC-RMTS can easily reduce the variable delay and significantly slow the growth of by-hop error accumulation. Thus, the proposed RDC-RMTS can achieve accurate time synchronization in large-scale complex WSNs.

Significant attention to multiple kernel graph-based clustering (MKGC) has emerged in recent years, primarily due to the superiority of multiple kernel learning (MKL) and the outstanding performance of graph-based clustering. However, many existing MKGC methods design a fat model that poses challenges for computational cost and clustering performance, as they learn both an affinity graph and an extra consensus kernel cumbersomely. To tackle this challenging problem, this article proposes a new MKGC method to learn a consensus affinity graph directly. By using the self-expressiveness graph learning and an adaptive local structure learning term, the local manifold structure of the data in kernel space is preserved for learning multiple candidate affinity graphs from a kernel pool first. After that, these candidate affinity graphs are synthesized to learn a consensus affinity graph via a thin autoweighted fusion model, in which a self-tuned Laplacian rank constraint and a top- k neighbors sparse strategy are introduced to improve the quality of the consensus affinity graph for accurate clustering purposes. The experimental results on ten benchmark datasets and two synthetic datasets show that the proposed method consistently and significantly outperforms the state-of-the-art methods.

Sidescan sonar image segmentation is a very important issue in underwater object detection and recognition. In this paper, a robust and fast method for sidescan sonar image segmentation is proposed, which deals with both speckle noise and intensity inhomogeneity that may cause considerable difficulties in image segmentation. The proposed method integrates the nonlocal means-based speckle filtering (NLMSF), coarse segmentation using k -means clustering, and fine segmentation using an improved region-scalable fitting (RSF) model. The NLMSF is used before the segmentation to effectively remove speckle noise while preserving meaningful details such as edges and fine features, which can make the segmentation easier and more accurate. After despeckling, a coarse segmentation is obtained by using k -means clustering, which can reduce the number of iterations. In the fine segmentation, to better deal with possible intensity inhomogeneity, an edge-driven constraint is combined with the RSF model, which can not only accelerate the convergence speed but also avoid trapping into local minima. The proposed method has been successfully applied to both noisy and inhomogeneous sonar images. Experimental and comparative results on real and synthetic sonar images demonstrate that the proposed method is robust against noise and intensity inhomogeneity, and is also fast and accurate.

Intending to mimic the operating mechanism of biological neural systems, a self organizing map-based approach to task assignment of a swarm of robots in 3-D dynamic environments is proposed in this paper. This approach integrates the advantages and characteristics of biological neural systems. It is capable of dynamically planning the paths of a swarm of robots in 3-D environments under uncertain situations, such as when some robots are presented in or broken down or when more than one robot is needed for some special task locations. A Bezier path optimizing algorithm and a parameter adjusting algorithm are integrated in this paper. It is capable of reducing the complexity of the robot navigation control and limiting the number of convergence iterations. The simulation results with different environments demonstrate the effectiveness of the proposed approach.

This paper proposes an efficient wayfinding strategy for robot navigation in regionalized environments by designing a regionalized spatial knowledge model (RSK model) and a region-based wayfinding algorithm, i.e., a fine-to-coarse A* (FTC-A*) search algorithm. First, the RSK model, which imitates the representation of environments in the human brain, is presented to describe the search environments. The environments that are divided into regions are represented by a hierarchical nested structure where small regions are grouped together to form superordinate regions. Second, on the basis of the RSK model, an FTC-A* search algorithm is developed to plan the fine-to-coarse route. By making a fine planning to robot surroundings in vicinity, but a coarse planning to that at the distance, the FTC-A* algorithm can effectively reduce computational complexity, so as to enhance the efficiency of route search, and meanwhile makes robots to react quickly to user's commands, especially in large-scale environments. Finally, four exhaustive simulations and a physical experiment have been carried out to illustrate the feasibility and effectiveness of the proposed wayfinding strategy.

Shortest path tree (SPT) computation is a critical issue for routers using link-state routing protocols, such as the most commonly used open shortest path first and intermediate system to intermediate system. Each router needs to recompute a new SPT rooted from itself whenever a change happens in the link state. Most commercial routers do this computation by deleting the current SPT and building a new one using static algorithms such as the Dijkstra algorithm at the beginning. Such recomputation of an entire SPT is inefficient, which may consume a considerable amount of CPU time and result in a time delay in the network. Some dynamic updating methods using the information in the updated SPT have been proposed in recent years. However, there are still many limitations in those dynamic algorithms. In this paper, a new modified model of pulse-coupled neural networks (M-PCNNs) is proposed for the SPT computation. It is rigorously proved that the proposed model is capable of solving some optimization problems, such as the SPT. A static algorithm is proposed based on the M-PCNNs to compute the SPT efficiently for large-scale problems. In addition, a dynamic algorithm that makes use of the structure of the previously computed SPT is proposed, which significantly improves the efficiency of the algorithm. Simulation results demonstrate the effective and efficient performance of the proposed approach.

For a 3-D underwater workspace with a variable ocean current, an integrated multiple autonomous underwater vehicle (AUV) dynamic task assignment and path planning algorithm is proposed by combing the improved self-organizing map (SOM) neural network and a novel velocity synthesis approach. The goal is to control a team of AUVs to reach all appointed target locations for only one time on the premise of workload balance and energy sufficiency while guaranteeing the least total and individual consumption in the presence of the variable ocean current. First, the SOM neuron network is developed to assign a team of AUVs to achieve multiple target locations in 3-D ocean environment. The working process involves special definition of the initial neural weights of the SOM network, the rule to select the winner, the computation of the neighborhood function, and the method to update weights. Then, the velocity synthesis approach is applied to plan the shortest path for each AUV to visit the corresponding target in a dynamic environment subject to the ocean current being variable and targets being movable. Lastly, to demonstrate the effectiveness of the proposed approach, simulation results are given in this paper.