Underwater scenarios are influenced by various factors such as light attenuation, scattering, and absorption, which degrade the quality of images and pose significant challenges for underwater object detection in marine research and ocean engineering. To address these challenges, we propose a novel adaptive-weight feature detection framework based on YOLOv8, called AWF-YOLO, designed to detect objects in turbid underwater scenarios accurately. AWF-YOLO incorporates several key components to improve detection performance. Firstly, a novel adaptive-weight feature pyramid network is introduced to facilitate the fusion of multi-scale feature semantics. In addition, an adaptive-weight feature extraction module is proposed to enhance underwater object detection by capturing relevant and discriminative information to enhance feature extraction further. We integrate a dedicated small object detection head into the detection network to overcome the challenges associated with detecting small objects in complex underwater scenarios. This component focuses on effectively identifying and localizing small objects, leading to improved overall detection accuracy. Extensive experiments conducted on the detection underwater objects dataset demonstrate that the proposed AWF-YOLO achieves significant performance improvements, thus making it highly suitable for complex and dynamic underwater scenarios.

This article investigates the practical stabilization problem of random quarter-car active suspension systems. An adaptive dynamic event-trigger strategy is proposed to stabilize the states of vehicle suspension in response to system uncertainty and controller area network resource constraints. Moreover, the model of random active suspension systems is extended to the general random robot systems; the controller is developed with the aid of a double dynamic surface filter, immersion and invariance (I&I) techniques, and event-triggered mechanisms. The results show that the semi-global stability of error systems is achieved, and there are some improvements in triggering times and adaptive estimation performance under the control framework. Finally, simulation comparison results are provided to prove the advantages of the proposed scheme.

Aero-engine is a complex thermal-mechanical system with strong nonlinearity, uncertainty, and time variation. Thus, it is crucial to design an effective controller for such a complex system to obtain the desired performances of the aero-engine. In recent years, model predictive control (MPC) has shown great potential in dealing with control problems with complex constraints of multi-variable systems, which has been applied to aero-engine control, achieving good results. Furthermore, the MPC strategy using an event-driven mechanism is good at balancing system resources and ensuring system control performances. In this paper, the problem of event-triggered MPC for aero-engine systems with bounded disturbances is studied. Firstly, an event-triggered strategy with a dynamic forced-trigger mechanism is proposed. Then, an MPC algorithm based on an event-triggered mechanism is designed. Finally, an application to the JT9D aero-engine model provided by T-MATS verifies the effectiveness of the designed algorithm. It is shown that the calculation load is significantly reduced, which proves the superiority of this method.

In this paper, the problem of optimal adaptive consensus tracking control for nonlinear multi-agent systems with prescribed performance is investigated. To address the issue of satisfying the initial value conditions in existing results, an improved performance function is employed as the prescribed performance boundary, effectively resolving this problem. Then, by employing the error transformation function, the constrained system is converted into an unconstrained one. Furthermore, fuzzy logic systems are employed to identify unknown system parts. By applying the dynamic surface technique, the problem of "differential explosion", which often occurs in backstepping, is solved. Moreover, a distributed optimal adaptive fuzzy control protocol based on the reinforcement learning actor-critic algorithm is proposed. Under the proposed control scheme, it is proved that all the signals within the closed-loop system are bounded, and the consensus tracking errors have remained within the predefined bounds. Finally, the numerical simulation results demonstrate the effectiveness of the proposed scheme.

Currently, there are a large number of tracking problems in the industry concerning nonlinear systems with unknown dynamics. In order to obtain the optimal control policy, a multi-step adaptive critic tracking control (MsACTC) algorithm is developed in this paper. By constructing a steady control law, the tracking problem is transformed into a regulation problem. The MsACTC algorithm has an adjustable convergence rate during the iterative process by incorporating a multi-step policy evaluation mechanism. The convergence proof of the algorithm is provided. In order to implement the algorithm, three neural networks are built, including the model network, the critic network, and the action network. Finally, two numerical simulation examples are given to verify the effectiveness of the algorithm. Simulation results show that the MsACTC algorithm has satisfactory performance in terms of the applicability, tracking accuracy, and convergence speed.

In this paper, a novel reward-based learning method is proposed for unmanned aerial vehicles to achieve multi-obstacle avoidance. The Markov jump model was first formulated for the unmanned aerial vehicle obstacle avoidance problem. A distinctive reward shaping function is proposed to adaptively avoid obstacles and finally reach the target position via an optimal approach such that an adaptive Q-learning algorithm called the improved prioritized experience replay is developed. Simulation results show that the proposed algorithm can achieve autonomous obstacle avoidance in complex environments with improved performance.

This paper investigates the problem of non-fragile state estimation for a class of reaction-diffusion genetic regulatory networks with mode-dependent time-varying delays and Markovian jump parameters. First, the Markov chain with partially unknown probabilities is used in this paper to describe the switching between system modes, which can make the model more generalizable. Moreover, considering the possible gain variations, we design a non-fragile state estimator that makes the estimation performance non-fragile to gain variations, thus guaranteeing the estimation performance. Sufficient conditions that ensure the asymptotic stability of the estimation error can be derived by using the Lyapunov stabilization theory and several inequality treatments. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed estimator design scheme.