Search Results (11)

Due to the singularity of Euler angles and the ambiguity of quaternions, to further expand the attitude reachable range of underactuated AUVs in the vertical plane, $SO\left(3\right)$ is used to represent the attitude change of underactuated AUVs. The transverse function of the attitude on $SO\left(3\right)$ is designed, and the exponential mapping method is used to construct the attitude kinematic controller of underactuated AUVs. Considering the changes in the model and ocean current during motion, interval type II fuzzy systems (IT2-FLSs) are used to estimate these changes. The backstepping method and the small gain theorem are adopted to design dynamic controllers to ensure the stability and robustness of the system. A novel saturation auxiliary system is designed to compensate for the influence of actuator saturation characteristics. Finally, the simulation results verify the effectiveness of the proposed controller and ensure the practical stabilization of the underactuated AUV attitude. Full article

Autonomous Underwater Vehicles (AUVs) play a significant role in ocean-related research fields as tools for human exploration and the development of marine resources. However, the uncertainty of the underwater environment and the complexity of underwater motion pose significant challenges to the fault-tolerant control of AUV actuators. This paper presents a fault-tolerant control strategy for AUV actuators based onTakagi and Sugeno (T-S) fuzzy logic and pseudo-inverse quadratic programming under control constraints, aimed at addressing potential actuator faults. Firstly, considering the steady-state performance and dynamic performance of the control system, a T-S fuzzy controller is designed. Next, based on the redundant configuration of the actuators, the propulsion system is normalized, and the fault-tolerant control of AUV actuators is achieved using the pseudo-inverse method under thrust allocation. When control is constrained, a quadratic programming approach is used to compensate for the input control quantity. Finally, the effectiveness of the fuzzy control and fault-tolerant control allocation methods studied in this paper is validated through mathematical simulation. The experimental results indicate that in various fault scenarios, the pseudo-inverse combined with a nonlinear quadratic programming algorithm can compensate for the missing control inputs due to control constraints, ensuring the normal thrust of AUV actuators and achieving the expected fault-tolerant effect. Full article

Autonomous Underwater Vehicles (AUVs) are widely used for the inspection of seabed pipelines. To address the issues of low trajectory tracking accuracy in AUV inspection processes due to uncertain ocean current disturbances, this paper designs a new dual-loop controller based on Model Predictive Control (MPC) and Variable Universe S-plane algorithms (S-VUD FLC, where VUD represents Variable Universe Discourse and FLC represents Fuzzy Logic Control) to achieve three-dimensional (3-D) trajectory tracking of an over-actuated AUV under uncertain ocean current disturbances. This paper uses MPC as the outer-loop position controller and S-VUD FLC as the inner-loop speed controller. The outer-loop controller generates desired speed instructions that are passed to the inner-loop speed controller, while the inner-loop speed controller generates control input and uses a direct logic thrust distribution method that approaches optimal energy consumption to distribute the thrust generated by the propellers to the over-actuated AUV, achieving closed-loop tracking of the entire trajectory. When designing the outer-loop MPC controller, the actual control input constraints of the system are considered, and control increments are introduced to reduce control model errors and the impact of uncertain external disturbances on the actual AUV model parameters. When designing the inner-loop S-VUD FLC, the strong robustness of the variable universe fuzzy controller and the easy construction characteristics of the S-plane algorithm are combined, and integral action is introduced to improve the system’s tracking accuracy. The stability of the outer loop controller is proven by the Lyapunov method, and the stability of the inner loop controller is verified by simulation. Finally, simulations show that the over-actuated AUV has fast tracking processes and high tracking result accuracy under uncertain ocean current disturbances, demonstrating the effectiveness of the designed dual-loop controller. Full article

How an autonomous underwater vehicle (AUV) performs fully automated task allocation and achieves satisfactory mission planning effects during the search for potential threats deployed in an underwater space is the focus of the paper. First, the task assignment problem is defined as a traveling salesman problem (TSP) with specific and distinct starting and ending points. Two competitive and non-commensurable optimization goals, the total sailing distance and the turning angle generated by an AUV to completely traverse threat points in the planned order, are taken into account. The maneuverability limitations of an AUV, namely, minimum radius of a turn and speed, are also introduced as constraints. Then, an improved ant colony optimization (ACO) algorithm based on fuzzy logic and a dynamic pheromone volatilization rule is developed to solve the TSP. With the help of the fuzzy set, the ants that have moved along better paths are screened and the pheromone update is performed only on preferred paths so as to enhance pathfinding guidance in the early stage of the ACO algorithm. By using the dynamic pheromone volatilization rule, more volatile pheromones on preferred paths are produced as the number of iterations of the ACO algorithm increases, thus providing an effective way for the algorithm to escape from a local minimum in the later stage. Finally, comparative simulations are presented to illustrate the effectiveness and advantages of the proposed algorithm and the influence of critical parameters is also analyzed and demonstrated. Full article

Autonomous underwater vehicles (AUVs) have broad applications owing to their ability to undertake long voyages, strong concealment, high level of intelligence and ability to replace humans in dangerous operations. AUV motion control systems can ensure stable operation in the complex ocean environment and have attracted significant research attention. In this paper, we propose a single-input fractional-order fuzzy logic controller (SIFOFLC) as an AUV motion control system. First, a single-input fuzzy logic controller (SIFLC) was proposed based on the signed distance method, whose control input is the linear combination of the error signal and its derivative. The SIFLC offers a significant reduction in the controller design and calculation process. Then, a SIFOFLC was obtained with the derivative of the error signal extending to a fractional order and offering greater flexibility and adaptability. Finally, to verify the superiority of the proposed control algorithm, comparative numerical simulations in terms of spiral dive motion control were conducted. Meanwhile, the parameters of different controllers were optimized according to the hybrid particle swarm optimization (HPSO) algorithm. The simulation results illustrate the superior stability and transient performance of the proposed control algorithm. Full article

The rapid technological development of computing power and system operations today allows for increasingly advanced algorithm implementation, as well as path planning in real time. The objective of this article is to provide a structured review of simulations and practical implementations of collision-avoidance and path-planning algorithms in autonomous underwater vehicles (AUVs). The novelty of the review paper is to consider not only the results of numerical research but also the newest results of verifying collision-avoidance and path-planning algorithms in real applications together with a comparison of the difficulties encountered during simulations and their practical implementation. Analysing the last 20 years of AUV development, it can be seen that experiments in a real environment are dominated by classical methods. In the case of simulation studies, artificial intelligence (AI) methods are used as often as classical methods. In simulation studies, the APF approach is most often used among classical methods, whereas among AI algorithms reinforcement learning and fuzzy logic methods are used. For real applications, the most used approach is reactive behaviors, and AI algorithms are rarely used in real implementations. Finally, this article provides a general summary, future works, and a discussion of the limitations that inhibit the further development in this field. Full article

In this study, a set of different search strategies for locating submarine groundwater discharge (SGD) are investigated. This set includes pre-defined path planning (PPP), adapted random walk (RW), particle swarm optimisation (PSO), inertia Levy-flight (ILF), self-organising-migration-algorithm (SOMA), and bumblebee search algorithm (BB). The influences of self-localisation and communication errors and limited travel distance of the autonomous underwater vehicles (AUVs) on the performance of the proposed algorithms are investigated. This study shows that the proposed search strategies could not outperform the classic search heuristic based on full coverage path planning if all AUVs followed the same search strategy. In this study, the influence of self-localisation and communication errors was investigated. The simulations showed that, based on the median error of the search runs, the performance of SOMA was in the same order of magnitude regardless the strength of the localisation error. Furthermore, it was shown that the performance of BB was highly affected by increasing localisation errors. From the simulations, it was revealed that all the algorithms, except for PSO and SOMA, were unaffected by disturbed communications. Here, the best performance was shown by PPP, followed by BB, SOMA, ILF, PSO, and RW. Furthermore, the influence of the limited travel distances of the AUVs on the search performance was evaluated. It was shown that all the algorithms, except for PSO, were affected by the shorter maximum travel distances of the AUVs. The performance of PPP increased with increasing maximum travel distances. However, for maximum travel distances > 1800 m the median error appeared constant. The effect of shorter travel distances on SOMA was smaller than on PPP. For maximum travel distances < 1200 m, SOMA outperformed all other strategies. In addition, it can be observed that only BB showed better performances for shorter travel distances than for longer ones. On the other hand, with different search strategies for each AUV, the search performance of the whole swarm can be improved by incorporating population-based search strategies such as PSO and SOMA within the PPP scheme. The best performance was achieved for the combination of two AUVs following PPP, while the third AUV utilised PSO. The best fitness of this combination was 15.9. This fitness was 26.4% better than the performance of PPP, which was 20.4 on average. In addition, a novel mechanism for dynamically selecting a search strategy for an AUV is proposed. This mechanism is based on fuzzy logic. This dynamic approach is able to perform at least as well as PPP and SOMA for different travel distances of AUVs. However, due to the better adaptation to the current situation, the overall performance, calculated based on the fitness achieved for different maximum travel distances, the proposed dynamic search strategy selection performed 32.8% better than PPP and 34.0% better than SOMA. Full article

This study proposes the development of an underwater object-tracking control system through an image-processing technique. It is used for the close-range recognition and dynamic tracking of autonomous underwater vehicles (AUVs) with an auxiliary light source for image processing. The image-processing technique includes color space conversion, target and background separation with binarization, noise removal with image filters, and image morphology. The image-recognition results become more complete through the aforementioned process. After the image information is obtained for the underwater object, the image area and coordinates are further adopted as the input values of the fuzzy logic controller (FLC) to calculate the rudder angle of the servomotor, and the propeller revolution speed is defined using the image information. The aforementioned experiments were all conducted in a stability water tank. Subsequently, the FLC was combined with an extended Kalman filter (EKF) for further dynamic experiments in a towing tank. Specifically, the EKF predicts new coordinates according to the original coordinates of an object to resolve data insufficiency. Consequently, several tests with moving speeds from 0.2 m/s to 0.8 m/s were analyzed to observe the changes in the rudder angles and the sensitivity of the propeller revolution speed. Full article

In this paper, an output-feedback fuzzy adaptive dynamic surface controller (FADSC) based on fuzzy adaptive extended state observer (FAESO) is proposed for autonomous underwater vehicle (AUV) systems in the presence of external disturbances, parameter uncertainties, measurement noises and actuator faults. The fuzzy logic system is incorporated into both the observers and controllers to improve the adaptability of the entire system. The dynamics of the AUV system is established first, considering the external disturbances and parameter uncertainties. Based on the dynamic models, the ESO, combined with a fuzzy logic system tuning the observer bandwidth, is developed to not only adaptively estimate both system states and the lumped disturbances for the controller, but also reduce the impact of measurement noises. Then, the DSC, together with fuzzy logic system tuning the time constant of the low-pass filter, is designed using estimations from the FAESO for the AUV system. The asymptotic stability of the entire system is analyzed through Lyapunov’s direct method in the time domain. Comparative simulations are implemented to verify the effectiveness and advantages of the proposed method compared with other observers and controllers considering external disturbances, parameter uncertainties and measurement noises and even the actuator faults that are not considered in the design process. The results show that the proposed method outperforms others in terms of tracking accuracy, robustness and energy consumption. Full article

The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project is to make autonomous underwater vehicles (AUVs), remote operated vehicles (ROVs) and unmanned surface vehicles (USVs) more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC) algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV’s parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC) algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the transmission power adjustment ratio while improving the network performance. Full article

In this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles), where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised. Furthermore, techniques based on several paradigms of the Artificial Intelligence (Neural Networks, Fuzzy systems and Genetic Algorithms) have been applied altogether in order to design an efficient fault diagnostic system. A hybrid Genetic Neuro-Fuzzy system has been developed, where it is possible, at the final stage of the learning process, to express the fault diagnostic system as a set of fuzzy rules. Several trials have been carried out and satisfactory results have been achieved. Full article