As the core of marine communication, underwater acoustic communication technology significantly improves the performance of underwater communication by using technologies such as modulation, multiple access, and anti-interference. Among them, adaptive filtering technology plays a key role in eliminating noise and channel equalization. This paper explores the novel application of acoustic reconfigurable intelligence surface (ARIS) and adaptive filtering in underwater acoustic communication. We introduce the concept of ARIS assisted underwater acoustic communication, integrating the least mean squares (LMS) algorithm to dynamically estimate and mitigate oceanic noise. This approach further enhances signal clarity and communication quality by leveraging orthogonal frequency division multiplexing (OFDM) technology. Simulation results show that the underwater acoustic channel with ARIS is superior to the traditional channel without ARIS in terms of bit error rate and anti-interference performance. This technology significantly enhances the reception quality of underwater communication signals, making it suitable for applications in marine monitoring, military communication, and other complex environments.

In this paper, we propose a novel noncausal control framework to address the energy maximization problem of wave energy converters (WECs) subject to constraints. The energy maximization problem of WECs is a constrained optimal control problem. The proposed control framework converts this problem into a reference trajectory tracking problem through the Fourier pseudo-spectral method (FPSM) and utilizes the online tracking adaptive dynamic programming (OTADP) algorithm to realize real-time trajectory tracking for practical use in the ocean environment. Using the wave prediction technique, the optimal trajectory is generated online through a receding horizon (RH) implementation. A critic neural network (NN) is applied to approximate the optimal cost value function and calculate the error-tracking control by solving the associated Hamilton-Jacobi-Bellman (HJB) equation. The proposed WEC control framework improves computational efficiency and makes the online control feasible in practice. Simulation results show the effects of the receding horizon implementation of FPSM with different window lengths and window functions, while verifying the performances of tracking control and energy absorption of WECs in two different sea conditions.

This paper proposes a novel intelligent fault detection method, i.e., combining the subspace identification technique (SIT) with the data-model interactive compensation mechanism for fault detection. More precisely, the design process is divided into two primary stages: offline learning process and real-time fault monitoring. In the offline learning phase, the fault isolation algorithm is employed, along with parameter identification using the subspace identification technique. Then the online data undergo normalization processing and are subjected to online fault detection using a trained offline model. In addition, considering the accuracy of fault detection, this paper designs the dynamic optimal threshold based on the fault detection rate (FDR) and the false alarm rate (FAR). In the end, the case studies on PT700 and Tennessee Eastman (TE) process data are provided to confirm that the proposed method achieves the fault detection objectives.

This study explores a new robust consensus control strategy for uncertain multiagent systems and provides an event-based solution to adaptive dynamic programming (ADP) based optimal control. Rather than the control function, the feedback system established symmetrical to the physical system allows the optimal consensus control issue to be handled by the optimal control protocol of an augmented affine system. The feedback system focuses on an auxiliary variable formed in light of the optimality principle and the virtual control input built on a critic neural network (NN). Analysis reveals that the auxiliary variable benefits from decreasing the influence of uncertainty on control performance, while the proposed approach is implemented with fewer communication resources since the critic NN is updated as events occur. Finally, evidence from simulation findings validates the theoretical results.

In this paper, we study the linear quadratic (LQ) optimal control of time-varying difference system with terminal state constraints. The main contribution is to provide the Q-learning algorithm for the optimal controller under the case that the time-varying system matrices and input matrices are both unknown, which consists of learning the solution of the Riccati equation and calculating the specific Lagrange multiplier from the data-driven matrix equation. Different from the existing Q-learning algorithms that mainly focus on unconstrained optimal control problems, the novelty of the proposed algorithm can be applied to handle situations with terminal state constraints. The effectiveness of the proposed Q-learning algorithm is demonstrated through a numerical example.

This paper uses parallel control to investigate the problem of tracking control of permanent magnet synchronous motors (PMSMs). First, an augmented PMSM system is designed to introduce the control input into the feedback system, and then achieve parallel control. Second, based on the backstepping control technique, the detailed steps of the derivation of the parallel controller are provided. The stability analysis shows that the tracking error of the PMSM system asymptotically converges to zero. Finally, the proposed parallel controller is validated in simulations, where the time-invariant reference signal and time-varying reference signal are considered, showing that the proposed parallel controller effectively enables the PMSM to track different reference signals.