Since precisely sensing the underwater environment is a challenging prerequisite for safe and reliable underwater operation, interest in underwater image processing is growing at a rapid pace. In engineering applications, there are redundant underwater images addressed in real-time on the remotely operated vehicle (ROV). It puts the equipment or operators under great pressure. To relieve this pressure by transmitting images selectively according to the degradation degree, we propose an end-to-end hybrid-input convolutional neural network (HI-CNN) to predict the degradation of underwater images. First, we propose a feature extraction module to extract the features of original underwater images and saliency maps concurrently, which is composed of two branches with the same structure and shared parameters. Second, we design an end-to-end model to predict the quality scores of original images, which consists of a feature extraction module and a prediction module. Finally, we establish a real-world dataset to make the proposed model be duplicated in the practical underwater environment. Through several experiments, we demonstrate that the proposed model outperforms existing models in predicting underwater image quality.

In this article, we investigate the problem of capturing a noncooperative target with deception behavior using reinforcement learning (RL) under incomplete information. The pursuer copes not only with its maneuverability constraint but also with the target’s deception behavior, in which the target deliberately conceals its private preference information. The target capture game involving deception behavior is formulated as a nonlinear differential game framework where the information structure is incomplete and asymmetric. The solution to this differential game is proposed based on an RL policy that incorporates critic, actor, and virtual actor neural networks (NNs), when taking into consideration the maneuverability constraint and information structure of the pursuer. Moreover, the states of the constrained adversarial system and the weight errors are proven to be ultimately uniformly bounded (UUB). To counter the deception of the target, we adopt unscented Kalman filter (UKF) to obtain the target intention on energy preference, and integrate it into the pursuer strategy. The feasibility of the proposed strategy and its superiority are verified through comparisons with recent works.

This article studies the distributed robust optimal control for discrete-time linear multiagent systems (MASs) with parametric uncertainties, where digraphs that only contain a directed spanning tree are allowed. Using the linear quadratic regulator approach, an optimal control protocol is presented. The presented controller is fully distributed, since the global information of graphs is unneeded for the design and implementation of the presented controller. The global performance index of MASs can be minimized by using the presented control protocol, and the optimal solution is independent with the information of parametric uncertainties. Finally, some simulated examples are provided to show the effectiveness of the proposed approaches.

This article is concerned with the robust cooperative optimal control of nonlinear multiagent systems (MASs) with external disturbances and modeling uncertainties. Using the super-twisting algorithm, a continuous sliding-mode control protocol is presented for high-order nonlinear MASs with multiple inputs. The sliding-mode dynamics is modeled by the Takagi-Sugeno fuzzy approach, and the nominal control protocol that guarantees the robust optimization of the cost function is designed. Directed topologies are allowed using the presented protocol, and many assumptions about topologies are removed. Finally, three numerical examples are reported to demonstrate the effectiveness and improved performance of the presented protocol.

This article develops a self-triggered adaptive neural network (NN) tracking controller for a class of continuous-time nonlinear systems, that is, input constrained and with unknown drift and input dynamics. Since the drift and input dynamics are both unknown, an NN is built within a self-triggered update paradigm to approximate the unknown tracking control. The error derivative used in the weight update algorithm is derived using a robust exact differentiator technique. To address input constraints, an auxiliary compensator is designed for the unimplemented control effort. Through rigorous Lyapunov analyses, we can guarantee that all the tracking and weight errors are uniformly ultimately bounded. Finally, to show the effectiveness of the proposed control performance, simulation results of a two-link robot are provided and analyzed.