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Ocean currents pose a significant challenge in the path planning of autonomous underwater vehicles (AUVs), with conventional path-planning algorithms often failing to effectively counter these influences. In response to this challenge, we propose a path-planning algorithm that can consider the influences and constraints of ocean currents, which leverages the strengths of two widely employed path-planning algorithms, A* and the genetic algorithm (GA), to account for the influences of ocean currents on the planned paths. Specifically, it enhances the initial population generation, formulates a fitness function tailored to ocean current conditions, and employs an adaptive mutation approach to enhance population diversity and stability. By utilizing simulated and real-world ocean current datasets, we validated the feasibility of the proposed algorithm with quantitative metrics. The results demonstrate that in comparison to conventional methods, the new algorithm can deal with the influences and constraints of ocean currents in AUV path planning, resulting in notable enhancements in path smoothness, energy efficiency, and safety.

Fault diagnosis of rolling bearings is a critical task, and in previous research, convolutional neural networks (CNN) have been used to process vibration signals and perform fault diagnosis. However, traditional CNN models have certain limitations in terms of accuracy. To improve accuracy, we propose a method that combines the Gramian angular difference field (GADF) with residual networks (ResNet) and embeds frequency channel attention module (Fca) in the ResNet to diagnose rolling bearing fault. Firstly, we used GADF to convert the signals into RGB three-channel fault images during data preprocessing. Secondly, to further enhance the performance of the model, on the foundation of the ResNet we embedded the frequency channel attention module with discrete cosine transform (DCT) to form Fca, to effectively explores the channel information of fault images and identifies the corresponding fault characteristics. Finally, the experiment validated that the accuracy of the new model reaches 99.3% and the accuracy reaches 98.6% even under an unbalanced data set, which significantly improves the accuracy of fault diagnosis and the generalization of the model.

For constrained image-based visual servoing (IBVS) of robot manipulators, a model predictive control (MPC) strategy tuned by reinforcement learning (RL) is proposed in this study. First, model predictive control is used to transform the image-based visual servo task into a nonlinear optimization problem while taking system constraints into consideration. In the design of the model predictive controller, a depth-independent visual servo model is presented as the predictive model. Next, a suitable model predictive control objective function weight matrix is trained and obtained by a deep-deterministic-policy-gradient-based (DDPG) RL algorithm. Then, the proposed controller gives the sequential joint signals, so that the robot manipulator can respond to the desired state quickly. Finally, appropriate comparative simulation experiments are developed to illustrate the efficacy and stability of the suggested strategy.

This study presents a novel image-based visual servoing fault-tolerant control strategy aimed at ensuring the successful completion of visual servoing tasks despite the presence of robotic arm actuator faults. Initially, a depth-independent image-based visual servoing model is established to mitigate the effects of inaccurate camera parameters and missing depth information on the system. Additionally, a robotic arm dynamic model is constructed, which simultaneously considers both multiplicative and additive actuator faults. Subsequently, model uncertainties, unknown disturbances, and coupled actuator faults are consolidated as centralized uncertainties, and an iterative learning fault observer is designed to estimate them. Based on this, suitable sliding surfaces and control laws are developed within the super-twisting sliding mode visual servo controller to rapidly reduce control deviation to near zero and circumvent the chattering phenomenon typically observed in traditional sliding mode control. Finally, through comparative simulation between different control strategies, the proposed method is shown to effectively counteract the effect of actuator faults and exhibit robust performance.

Abstract To ensure the power quality of a ship power system under disturbances due to changing sea states, an adaptive bus‐voltage controller with online load power fluctuation prediction is proposed to stabilize the ship bus voltage. The controller uses an online sequence extreme learning machine to update the output weight matrix and achieve an accurate online prediction of the ship load power fluctuations under the disturbance of random uncertainties. A stable additional control signal is provided to the bus‐voltage controller for adaptive operation. The prediction results are used to optimize the coordinated control of multiple generation systems and improve the bus‐voltage control performance by updating the droop control coefficients online. The adaptability provided by the online prediction algorithm and the performance of the bus‐voltage control are verified using recorded voyage data of an offshore marine engineering transport vessel.

Abstract This study proposes a power management strategy (PMS) that can efficiently and accurately address the nonlinear dynamics of ship power systems for all‐electric ships (AESs). The design of a PMS can be considered naturally in a model predictive control framework. However, the control error caused by a model mismatch is a challenge of the PMS for physical system implementation. The proposed PMS is based on a complex system‐level nonlinear model that ensures a minimum mismatch. In addition, an efficient optimization algorithm is proposed to efficiently apply the PMS. The proposed method is verified using real‐time simulations with physical systems in various scenarios. The experimental results demonstrate the superior efficiency of the proposed optimization algorithm and the accuracy of the developed PMS for physical system implementation.

As an effective way to achieve energy conservation and emission reduction, the speed optimization of inland ships has received increasing attention. In this study, a modeling method for the speed optimization of inland ships sailing on the Yangtze River and a heuristic algorithm called Augmented Lagrangian Differential Evolution (ALDE) method are proposed. The ALDE introduces adaptive control parameters, an elite strategy, and opposition-based learning technology for optimization. To validate the performance of ALDE, it is compared with six other algorithms to solve several well-known constrained optimization problems, and the results show that the ALDE outperforms these six algorithms. To construct a speed optimization model, a discretization model for the Yangtze River waterway is established by segmenting the waterway. Considering the relationship among the environment, ship, engine, and propeller, a fuel consumption prediction model is built, and then, the objective optimization function for inland ship speed optimization is established by integrating the discrete model as well as the fuel consumption model. Finally, numerical simulation results show that the ALDE can realize less fuel consumption for an inland ship sailing from Yibin Port to Jiangyin Port, verifying the feasibility of the ALDE for speed optimization of inland ships.

A new image-based robot visual servo control strategy based on a third-order sliding-mode observer (TOSM) model predictive control is proposed in this study. This new control strategy solves the problem of robot visual servo control with system constraints and time-varying disturbances when the camera and model of the robot manipulator are uncertain and the joint velocity is unknown. In the proposed method, the joint velocity and system centralized uncertainties are estimated simultaneously based on a third-order sliding-mode observer, and the image-based visual servoing problem is transformed into a nonlinear optimization problem based on a model predictive control method considering both visibility constraints and actuator constraints, which minimizes the predicted trajectory cost function to generate the control signal for each cycle. Simulations were carried out to verify the effectiveness of the proposed control scheme.

This paper investigates the problem of full-order and reduced-order fault detection filter (FDF) design under unified linear matrix inequality (LMI) conditions for a class of continuous-time singular Markovian jump systems (CTSMJSs) with time-varying delays and polytopic uncertain transition rates. By constructing a new Lyapunov function, sufficient conditions are firstly provided for the singular model error augmented system such that the system is stochastically admissible with an H∞ performance level γ. And then, by applying a novel convex polyhedron technique to decoupled linear matrix inequalities, the full-order and reduced-order fault detection filter parameters can be obtained within a convex optimization frame. The reduced-order fault detection filter (FDF) can not only meet the fault detection accuracy requirements of complex systems but also improve the fault detection efficiency. Finally, a DC motor and an illustrative simulation example are given to verify the feasibility and effectiveness of the proposed algorithms.

With the in-depth penetration of renewable energy in the shipboard power system, the uncertainty of its output power and the variability of sea conditions have brought severe challenges to the control of shipboard integrated power system. In order to provide additional accurate signals to the power control system to eliminate the influence of uncertain factors, this study proposed an adaptive kernel based online sequential extreme learning machine to accurately predict shipboard electric power fluctuation online. Three adaptive factors are introduced, which control the kernel function scale adaptively to ensure the accuracy and speed of the algorithm. The electric power fluctuation data of real-ship under two different sea conditions are used to verify the effectiveness of the algorithm. The simulation results clearly demonstrate that in the case of ship power fluctuation prediction, the proposed method can not only meet the rapidity demand of real-time control system, but also provide accurate prediction results.

Shipboard integrated power systems, the key technology of ship electrification, call for effective failure mode power management control strategy to achieve the safe and reliable operation in dynamic reconfiguration. Considering switch reconfiguration with system dynamics and power balance restoration after reconfiguration, in this paper, the optimization objective function of optimal management for ship failure mode is established as a hybrid model predictive control problem from the perspective of hybrid system. To meet the needs for fast computation, a hierarchical hybrid model predictive control algorithm is proposed, which divides the original optimization problem into two stages, and reduces the computation complexity by relaxing constraints and the minimum principle. By applying to a real-time simulator in two scenarios, the results verify the effectivity of the proposed method.

When a solar ship is navigating in the ocean, the swaying motion of a photovoltaic panel will affect the output power of the photovoltaic (PV) power generation system more frequently and violently. In addition to considering multiple climatic factors, this paper also adopts a ship swaying motion and radiation level of sunlight to establish a suitable calculation model for the output power of photovoltaic systems, which are rarely considered at the same time in previous studies, and also to make ultrashort-term power predictions. Furthermore, this paper proposes a multilayer heterogeneous particle swarm optimization (PSO) algorithm to design the weights and thresholds of a long short-term memory (LSTM) neural network to improve the accuracy of forecasting the changes of a photovoltaic panel’s angle, which is used for accurate power output prediction for the purpose of power planning. The case analysis shows the effectiveness of the algorithm, which provides a more reliable method for designing a power prediction system.

The fault detection (FD) reduced-order filtering problem is investigated for a family of polytopic uncertain discrete-time Markovian jump linear systems (MJLSs) with time-varying delays. Under meeting the control precision requirements of the complex systems, the reduced-order fault detection filter can improve the efficiency of the fault detection. Then, by the aid of the Markovian Lyapunov function and convex polyhedron techniques, some novel time-varying delays and polytopic uncertain sufficient conditions in terms of linear matrix inequality (LMI) are proposed to insure the existence of the FD reduced-order filter. Finally, an illustrative example is provided to verify the usefulness of the given method.

We study the Cauchy problem for a sixth-order Boussinesq equations with the generalized source term and damping term. By using Galerkin approximations and potential well methods, we prove the existence of a global weak solution. Furthermore, we study the conditions for the damped coefficient to obtain the finite time blow up of the solution.

The fault detection (FD) reduced-order filtering problem is investigated for a family of continuous-time Markovian jump linear systems (MJLSs) with polytopic uncertain transition rates, which also include the totally known and partly unknown transition rates. Then, in accordance with the convexification techniques, a novel sufficient condition for the existence of FD reduced-order filter over MJLSs with deficient transition information is obtained in terms of linear matrix inequality (LMI), which can ensure the error augmented system with the FD reduced-order filter is randomly stable. In addition, a performance index is given to enhance the robustness of the residual system against deficient transition information and external disturbance, such that the error between the fault and the residual is made as small as possible to reinforce the faults sensitivity. Finally, the effectiveness of the proposed method is substantiated with two illustrative examples.