Adaptive Sensor-Fault Tolerant Control of Unmanned Underwater Vehicles With Input Saturation

This paper investigates the tracking control problem for unmanned underwater vehicles(UUVs)systems with sensor faults,input saturation,and external disturbance caused by waves and ocean currents.An active sensor fault-tolerant control scheme is proposed.First,the developed method only requires the inertia matrix of the UUV,without other dynamic information,and can handle both additive and multiplicative sensor faults.Subsequently,an adaptive fault-tolerant controller is designed to achieve asymptotic tracking control of the UUV by employing robust integral of the sign of error feedback method.It is shown that the effect of sensor faults is online estimated and compensated by an adaptive estimator.With the proposed controller,the tracking error and estimation error can asymptotically converge to zero.Finally,simulation results are performed to demonstrate the effectiveness of the proposed method.

Multiple Unmanned Underwater Vehicles Consensus Control with Unmeasurable Velocity Information and Environmental Disturbances Under Switching Directed Topologies

A consensus algorithm proposed in the paper is applied to tackle remarkable problems of unmeasurable velocities,the environmental disturbances, and the limited communication environment for the multiple unmanned underwater vehicles(multi-UUVs). Firstly, for a complex nonlinear and coupled model of the unmanned underwater vehicle(UUV), a technique of feedback linearization is developed to transform the nonlinear UUV model into a secondorder integral UUV model. Secondly, to address the problem of the unavailable velocity information and environmental disturbances for the multi-UUVs system, we design a distributed extended state observer(DESO) to estimate the unmeasurable velocities and environmental disturbances using the relative position information. Finally,we propose a protocol based on the estimation information from the DESO and demonstrate that the multi-UUVs system with the switching directed topologies under the protocol can reach consensus asymptotically. The theoretical result proposed in the literature is verified by one numerical example.

Using Unmanned Underwater Vehicles as Research Platforms in Coastal Ocean Studies

The advantages of using unmanned underwater vehicles in coastal ocean studies are emphasized. Two types of representative vehicles, remotely operated vehicle (ROV) and autonomous underwater vehicle (AUV) from University of South Florida, are discussed. Two individual modular sensor packages designed and tested for these platforms and field measurement results are also presented. The bottom classification and albedo package, BCAP, provides fast and accurate estimates of bottom albedos, along with other parameters such as in-water remote sensing reflectance. The real-time ocean bottom optical topographer, ROBOT, reveals high-resolution 3-dimentional bottom topography for target identification. Field data and results from recent Coastal Benthic Optical Properties field campaign, 1999 and 2000, are presented. Advantages and limitations of these vehicles and applications of modular sensor packages are compared and discussed.

Submarine Hunter: Efficient and Secure Multi-Type Unmanned Vehicles

Utilizing artificial intelligence(AI)to protect smart coastal cities has become a novel vision for scientific and industrial institutions.One of these AI technologies is using efficient and secure multi-environment Unmanned Vehicles(UVs)for anti-submarine attacks.This study’s contribution is the early detection of a submarine assault employing hybrid environment UVs that are controlled using swarm optimization and secure the information in between UVs using a decentralized cybersecurity strategy.The Dragonfly Algorithm is used for the orientation and clustering of the UVs in the optimization approach,and the Re-fragmentation strategy is used in the Network layer of the TCP/IP protocol as a cybersecurity solution.The research’s noteworthy findings demonstrate UVs’logistical capability to promptly detect the target and address the problem while securely keeping the drone’s geographical information.The results suggest that detecting the submarine early increases the likelihood of averting a collision.The dragonfly strategy of sensing the position of the submersible and aggregating around it demonstrates the reliability of swarm intelligence in increasing access efficiency.Securing communication between Unmanned Aerial Vehicles(UAVs)improves the level of secrecy necessary for the task.The swarm navigation is based on a peer-to-peer system,which allows each UAV to access information from its peers.This,in turn,helps the UAVs to determine the best route to take and to avoid collisions with other UAVs.The dragonfly strategy also increases the speed of the mission by minimizing the time spent finding the target.

Experimental Study of a Modified Command Governor Adaptive Controller for Depth Control of an Unmanned Underwater Vehicle

Command governor–based adaptive control(CGAC)is a recent control strategy that has been explored as a possible candidate for the challenging task of precise maneuvering of unmanned underwater vehicles(UUVs)with parameter variations.CGAC is derived from standard model reference adaptive control(MRAC)by adding a command governor that guarantees acceptable transient performance without compromising stability and a command filter that improves the robustness against noise and time delay.Although simulation and experimental studies have shown substantial overall performance improvements of CGAC over MRAC for UUVs,it has also shown that the command filter leads to a marked reduction in initial tracking performance of CGAC.As a solution,this paper proposes the replacement of the command filter by a weight filter to improve the initial tracking performance without compromising robustness and the addition of a closed-loop state predictor to further improve the overall tracking performance.The new modified CGAC(M-CGAC)has been experimentally validated and the results indicate that it successfully mitigates the initial tracking performance reduction,significantly improves the overall tracking performance,uses less control force,and increases the robustness to noise and time delay.Thus,M-CGAC is a viable adaptive control algorithm for current and future UUV applications.

Hydrodynamic Modeling with Grey-Box Method of A Foil-Like Underwater Vehicle

In this study, a dynamic modeling method for foil-like underwater vehicles is introduced and experimentally verified in different sea tests of the Hadal ARV. The dumping force of a foil-like underwater vehicle is sensitive to swing motion. Some foil-like underwater vehicles swing periodically when performing a free-fall dive task in experiments. Models using conventional modeling methods yield solutions with asymptotic stability, which cannot simulate the self-sustained swing motion. By improving the ridge regression optimization algorithm, a grey-box modeling method based on 378 viscous drag coefficients using the Taylor series expansion is proposed in this study. The method is optimized for over-fitting and convergence problems caused by large parameter matrices. Instead of the PMM test data, the unsteady computational fluid dynamics calculation results are used in modeling. The obtained model can better simulate the swing motion of the underwater vehicle. Simulation and experimental results show a good consistency in free-fall tests during sea trials, as well as a prediction of the dive speed in the swing state.

Leader-following coordination of multiple UUVs formation under two independent topologies and time-varying delays

The new method which uses the consensus algorithm to solve the coordinate control problems of multiple unmanned underwater vehicles （multi-UUVs） formation in the case of leader-following is adapted. As the communication between the UUVs is difficult and it is easy to be interfered under the water, time delay is assumed to be time-varying during the members communicate with each other. Meanwhile, the state feedback linearization method is used to transfer the nonlinear and coupling model of UUV into double-integrator dynamic. With this simplified double-integrator math model, the UUV formation coordinate control is regarded as consensus problem with time-varying communication delays. In addition, the position and velocity topologies are adapted to reduce the data volume in each data packet which is sent between members in formation. With two independent topologies designed, two cases of communication delay which are same and different are considered and the sufficient conditions are proposed and analyzed. The stability of the multi-UUVs formation is proven by using Lyapunov-Razumilkhin theorem. Finally, the simulation results are presented to confirm and illustrate the theoretical results.

A hybrid tracking control strategy for an unmanned underwater vehicle aided with bioinspired neural dynamics

Tracking control has been a vital research topic in robotics.This paper presents a novel hybrid control strategy for an unmanned underwater vehicle(UUV)based on a bio-inspired neural dynamics model.An enhanced backstepping kinematic control strategy is first developed to avoid sharp velocity jumps and provides smooth velocity commands relative to conventional methods.Then,a novel sliding mode control is proposed,which is capable of providing smooth and continuous torque commands free from chattering.In comparative studies,the proposed combined hybrid control strategy has ensured control signal smoothness,which is critical in real‐world applications,especially for a UUV that needs to operate in complex underwater environments.