This article investigates the distributed resilient energy management (EM) strategy for the seaport microgrid under stealthy attacks. First, based on an analysis of seaport microgrid characteristics, we construct an EM model that aims at minimizing both operating cost and security defense resource (SDR) cost. Second, we present a distributed, resilient strategy by defining node security levels and establishing dynamic security intervals. We prove that the gap between the obtained feasible solution and the optimal one is bounded. The designed strategy is capable of tolerating the effect of the unlimited number of stealthy attacked nodes on the seaport microgrid. In addition, given the limited SDRs for the resilient EM of the island seaport microgrid, a distributed mechanism for searching the minimum security connected dominating set (MSCDS) is proposed to minimize the size of trusted nodes. Finally, simulation results demonstrate the effectiveness of the proposed strategy. Note to Practitioners: This article addresses the vulnerability of the seaport microgrid, a critical issue that impacts EM and disrupts seaport operations. Current approaches to seaport EM do not account for potential attacks. Meanwhile, existing methods for attack resilience often overlook the costs of security resources. We propose a new approach for the distributed and resilient EM of the island seaport microgrid. Secure operation is achieved by protecting the fewest trusted nodes, thereby conserving SDRs. We then show how this algorithm (searching the MSCDS) can be efficiently designed. Preliminary simulations indicate its feasibility, though it has yet to be tested in a production environment. Future research will focus on designing trusted nodes within dynamic topologies.

This article presents an adaptive prescribed-time dynamic surface control approach for a differential-driven unmanned surface vehicle (USV) with fully unknown parameters, which is a cascaded control system consisting of kinematic, kinetic, and motor-driven propeller layers. First, the control design of the kinematic layer determines the virtual surge and yaw velocities, which are filtered as the pseudocommands for the kinetic layer. By imposing a performance function for the position error of the USV, it can be driven into the preset precision region within the prescribed time. Second, the virtual force control laws designed in the kinetic layer give the desired motor angular speed commands, which are filtered as the pseudocommands for the propeller layer. Then, in the control design of the propeller layer, the actual command duty cycles are proposed to make the actual motor angular speed follow these pseudospeed commands of motors to apply in practice directly. By employing the fuzzy logic system to approximate the nonlinearities, we can establish a simpler control structure than the existing disturbance-observer-based controller due to the avoidance of virtual control laws and the adaptive compensations of modeling uncertainties. Finally, experimental results show that the proposed method makes the tacking task successful with user-defined performance.

Accurate performance and low energy consumption are essential for trajectory tracking control of an underactuated surface vessel (USV) to satisfy marine operation requirements. This paper studies the user-specified optimal tracking control problem for the USV in the presence of modeling uncertainties. A fuzzy weights-based adaptive reinforcement learning (RL) control scheme is developed to achieve the tracking control with the predefined convergence time and steady-state accuracy while minimizing the performance index function. The constructed performance index function includes the surge velocity control law and the constrained position tracking error. Following that, an optimal backstepping design frame is proposed to derive the controllers with unknown gradient terms by constructing the error subsystems at the kinematic and kinetic levels of the USV. Then, a fuzzy reinforcement learning algorithm (FRLA) is presented to derive the optimal control solutions of corresponding error subsystems. Since the fuzzy basis function vectors derived from measured states of the USV system may be zero vectors, the persistence excitation condition may not always be satisfied. Thus, in the design of fuzzy RL updating laws of critics and actors, a positive parameter compensation technique is proposed to make the matrixes consist of fuzzy basis functions strictly positive definite. It releases the need for assumption of persistence excitation, and the convergence of the critic weight estimate and the boundedness of closed-loop systems can be guaranteed. Finally, the effectiveness of the presented method is demonstrated by numerical simulations and real-world experiments.

As the number of ships docking at ports and using onshore power grows, there is a pressing need for efficient economic dispatch within the port microgrid (PMG). The computing power network (CPN) leverages ubiquitous computing resources, presenting new opportunities for handling complex tasks, such as the economic dispatch problem (EDP) more efficiently. However, integrating CPNs into PMGs raises significant concerns about data security and privacy. To address these issues, this article proposes a distributed prescribed-time optimization (PPDPTO) algorithm specifically designed for the EDP in the integrated PMG and CPN. This algorithm ensures both high computation efficiency and enhanced privacy security. Specifically, this algorithm combines the distributed prescribed-time optimization theory and the privacy-preserving technique, which can protect the sensitive energy information of the berthing ships while ensuring fast convergence. Moreover, based on the proposed mask function protecting the power demand information of the berthing ships, the optimal solution of the EDP can be obtained with limited decoding resources. A smooth piecewise mask function is proposed to promote the nonvulnerability of the system and the bias in the optimal steady states of the proposed algorithm is reduced. Furthermore, the prescribed-time convergence of the PPDPTO algorithm is proven; meanwhile, the supply and demand balance constraints of the EDP can be guaranteed under the mask function. Finally, simulations undermine the effectiveness of the PPDPTO algorithm.

A distributed optimization problem based on a continuous-time multiagent system (MAS) disturbed by general noise is considered in this paper. The general noise, under some relaxed assumptions, which may be a stationary process, is proposed to describe the disturbance among agents more accurately. The noise-to-state (NOS) stability of the concerned MAS is analyzed based on an improved theoretical result of random differential equations. Furthermore, the relative sufficient conditions in the form of linear matrix inequality are developed with less conservatism, from which the minimum estimation error between the optimal solution and the NOS stable state of the proposed MAS with general noise can be obtained by choosing some appropriate distributed optimization parameters. One example is used to verify the effectiveness of the proposed approach.