Control Of Underwater Vehicle Research Articles

Formation control of underwater vehicles using Multi Agent System

Formation control of underwater vehicles using Multi Agent System

Robust trajectory tracking control scheme using transformed velocities for asymmetric underactuated marine vehicles

The paper addresses the issue of trajectory tracking control for underactuated underwater vehicles whose model is described by quasi-velocities resulting from the decomposition of the inertia matrix. The control algorithm takes into account unmodeled dynamics and external disturbances. It is shown that such mathematical models can be diagonalized and described by inertial quasi-velocities (IQV). The control scheme consists of a kinematic velocity controller and a dynamic adaptive integral sliding mode control algorithm. The paper extends the concept based on velocity transformation and backstepping methods to systems with a symmetric inertia matrix. A proof of the stability of a closed system in IQV space was carried out. The proposed approach was verified on two 3 DOF models of underwater vehicles with constraints due to the application of thruster force limitations.

Adaptive Finite-Time Trajectory Tracking Control for Coaxial HAUVs Facing Uncertainties and Unknown Environmental Disturbances

In this paper, the problems of system design, dynamic modeling, and trajectory tracking control of coaxial hybrid aerial underwater vehicles (HAUVs) with time-varying model parameters and composite marine environment disturbances are investigated. It is clear that a stable transition between different media remains a challenge in the practical implementation of amphibious tasks. For HAUVs, accurate dynamic modeling to describe complex dynamic variations during vehicle takeoff from underwater to air is a huge challenge. Meanwhile, due to the rapid changes in model parameters and the external environment, vehicles are likely to fall into the sea during the cross-domain process. An integrated continuous dynamic model considering hydrodynamic changes is established by introducing a linear switching coefficient during the process of trans-medium motion. A nonsingular fast terminal sliding-mode control (NFTSMC) algorithm combined with adaptive technology is used to design the position and attitude of the controller. With no previous knowledge of external interferences and lumped uncertainties of the HAUV, the adaptive NFTSMC (ANFTSMC) algorithm achieves the control objectives; at the same time, the inherent chattering problems of sliding mode control (SMC) are weakened. The finite-time stability of the global system is proven strictly using a series of mathematical derivations based on Lyapunov theory. The effect of the controller applied is analyzed through a series of simulations with representative working conditions. The results show that the proposed ANFTSMC can realize a “seamless” air–water trans-medium process, which proves the superiority and robustness of the proposed control algorithm.

Three-Dimensional Prescribed Performance Tracking Control of UUV via PMPC and RBFNN-FTTSMC

To address the search-and-docking problem in multi-stage prescribed performance switching (MPPS) scenarios, this paper presents a novel compound control method for three-dimensional (3D) underwater trajectory tracking control of unmanned underwater vehicles (UUVs) subjected to unknown disturbances. The proposed control framework can be divided into two parts: kinematics control and dynamics control. In the kinematics control loop, a novel parallel model predictive control (PMPC) law is proposed, which is composed of a soft-constrained model predictive controller (SMPC) and hard-constrained model predictive controller (HMPC), and utilizes a weight allocator to enable switching between soft and hard constraints based on task goals, thus achieving global optimal control in MPPS scenarios. In the dynamics control loop, a finite-time terminal sliding mode control (FTTSMC) method combining a finite-time radial basis function neural network adaptive disturbance observer (RBFNN-FTTSMC) is proposed to achieve disturbance estimation and fast convergence of velocity tracking errors. The simulation results demonstrate that the proposed PMPC-FTTSMC approach achieved an average improvement of 33% and 80% in the number of iterations compared with MPC with sliding mode control (MPC-SMC) and traditional MPC methods, respectively. Furthermore, the approach improved the speed of response by 35% and 44%, respectively, while accurately achieving disturbance observation and enhancing the system robustness.

Continuous–Discrete Observation-Based Robust Tracking Control of Underwater Vehicles: Design, Stability Analysis, and Experiments

This study addresses the tracking control problem of underwater vehicles using a new robust observation-based control scheme. The advantages of the robust integral of the sign of the error (RISE) control, as well as the saturation function and well-known super-twisting algorithm, have been exploited to design a saturated super-twisting RISE (S <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{+}$</tex-math> </inline-formula> RISE) control scheme. However, the proposed S <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{+}$</tex-math> </inline-formula> RISE method requires continuous state measurements. To resolve this issue, a continuous–discrete time observer (CDO) is proposed, which works in tandem with the proposed controller. The resulting control scheme is known as CDO-S <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{+}$</tex-math> </inline-formula> RISE. In addition to estimating disturbances, the proposed CDO solves the problem of multiple sampling rates of the sensors. To demonstrate the asymptotic stability of the resulting nonobservation-based closed-loop dynamics with the proposed S <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{+}$</tex-math> </inline-formula> RISE control scheme, Lyapunov arguments are proposed. Then, the exponential stability of the unperturbed closed loop with the proposed CDO, as well as with the proposed S <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{+}$</tex-math> </inline-formula> RISE controller, is studied based on the Lyapunov–Krasovskii concept. To verify the performance recovery of the overall observation-based closed-loop system CDO-S <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{+}$</tex-math> </inline-formula> RISE (controlled by the proposed S <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{+}$</tex-math> </inline-formula> RISE control scheme), an invariant set <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{A}_{\mathbb{R}}$</tex-math> </inline-formula> is determined using a composite Lyapunov–Krasovskii functional, which guarantees the convergence of the tracking errors to the origin. Several real-time experimental scenarios were conducted on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Leonard</i> underwater vehicle prototype to validate the efficiency and robustness of the proposed CDO-S <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{+}$</tex-math> </inline-formula> RISE scheme.

Distributed robust model predictive control-based formation-containment tracking control for autonomous underwater vehicles

The autonomous underwater vehicle (AUV) formation may be carried out in a complex maritime environment. The formation-containment tracking problem is faced with challenges due to limited input saturation and maritime safety constraints. Based on communication-measurement union, a navigation control integration framework is proposed to solve the poor observability of underwater acoustic communication interactions. Moreover, a control protocol based on distributed robust model predictive control is designed to efficiently coordinate cooperation between AUVs. With the proof of the Lyapunov theory, the proposed control method can achieve formation-containment tracking even in the presence of external disturbances. Finally, a 7-AUV formation-containment tracking numerical simulation is designed to verify the effectiveness of the proposed method.

ELIMINATION OF THE INFLUENCE OF WIND-WAVE DISTURBANCES ON THE MOVEMENT OF THE UNDERWATER VEHICLE USING LOW-FREQUENCY FILTERING

The article is devoted to the problem of the influence of wind-wave disturbances on the quality of control of an uninhabited underwater vehicle. The problem is urgent, since as a result of such an impact, there is an increase in the cost of electricity for the operation of the engine, which pumps and pumps water into the ballast chamber of the underwater vehicle to maintain it at a given depth, which generally affects the reduction of the time the device stays underwater. In the course of the study, the influence of the wind wave disturbance was assessed depending on its score, and a means was proposed to eliminate excessive energy costs in the form of low-frequency filtration.

Dynamic event-triggered observer-based control for autonomous underwater vehicles in the Trans-Atlantic Geotraverse hydrothermal field using rotation matrices

The issue of dynamic event-triggered trajectory tracking control of autonomous underwater vehicles (AUVs) in the Trans-Atlantic Geotraverse (TAG) active mound is addressed. Concretely, a novel adaptive event-triggered disturbance observer along with auxiliary functions is constructed to handle with actuator faults and ocean current in the TAG active mound. By using rotation matrices, a new tracking error system involving position and attitude is developed such that potential singularities caused by the large pitch will be sufficiently avoided. By deep combination with model parameters, a sliding mode manifold based on additional velocity errors is established to promote robustness and convergence. Following the detailed analysis, sufficient conditions for the input-to-state practical stability in mean square are given to ensure the tracking performance. An adaptive dynamic event-trigger method is applied to reduce the communication frequency between the embedded PC module and actuators. Finally, the applications of an AUV tracking system in the near-bottom hydrothermal mound are carried out to verify the efficiency of the presented method.

Autonomous underwater vehicle motion state recognition and control pattern mining

Self-awareness of its own state during autonomous operation is critical for Autonomous Underwater Vehicles (AUVs) to execute tasks and monitor their health. Automated data analysis methods appear to be the most appropriate tool for attempting to understand and elucidate the motion states of AUVs. However, research scholars often focus on the state recognition of small AUVs used for shallow water detection, and the relevant conclusions cannot be directly applied to deep-sea AUVs. As a representative deep-sea AUV, Qianlong-3 is studied in this paper. In this study, a data-driven approach was employed to recognize the motion states and control modes of the Qianlong-3 AUV while retaining the original data noise. First, the class imbalance problem in the data was addressed, and then the data features were enhanced through a sliding window statistical method. By comparing the differences in the accuracy of motion state recognition after class imbalance processing using different supervised learning methods, the Random Forest (RF) algorithm was found to be the most suitable classification algorithm. In addition, the Toeplitz Inverse Covariance-Based Clustering (TICC) algorithm was used to recognize and mine the motion states and control modes of the AUV. The TICC algorithm was effective in extracting latent information from the actual data. Finally, through correlation analysis, the three motion states of the AUV were discussed, and the control modes for each of these states were determined. Overall, this study demonstrates the feasibility of relevant algorithms in practical deep-sea applications for state recognition and control mode mining. This provides valuable reference and experience for the development of future deep-sea AUV.

Piecewise Compensation Model Predictive Governor Combined With Conditional Disturbance Negation for Underactuated AUV Tracking Control

With respect to the accurate tracking control for autonomous underwater vehicles (AUVs), this paper proposes a novel control architecture consisting of piecewise compensation model predictive governor and conditional disturbance negation-based active disturbance rejection controller. The designed piecewise compensation mechanism is implemented to eliminate the steady tracking errors resulting from the underactuation problem of AUVs, and the model predictive governor aims to generate reference tracking velocities satisfying the constraints. Concurrently, a finite-time extended state observer is designed to improve the convergence performance of the estimation errors. In addition, the concept of conditional disturbance negation is employed to endow AUVs with the capability of taking advantage of disturbances rather than only rejecting disturbances. Finally, comprehensive analyses of the simulations and the experiments demonstrate the impressive control performance, verifying the feasibility of potential applications on real AUVs.

Maximum likelihood interval-varying recursive least squares identification for output-error autoregressive systems with scarce measurements

The identification problem of output-error autoregressive (OEAR) systems with scarce measurements is considered in this paper. In order to overcome the massive absence of outputs, an interval-varying recursive identification algorithm is proposed through changing the sampling interval and skipping the missing outputs. Based on the maximum likelihood principle, a maximum likelihood interval-varying recursive least squares algorithm is proposed. The effectiveness of the proposed algorithm is tested by a numerical simulation example, and an application example about the heading motion control of underwater vehicle.

Cooperative learning formation control of multiple autonomous underwater vehicles with prescribed performance based on position estimation

This paper proposes a cooperative learning formation control method with finite-time prescribed performance based on position estimation for parametric path tracking of multiple autonomous underwater vehicles (AUVs) with uncertainties and external disturbances. The parametric path used in the control law permits the velocity to be specified independently while tracking the path accurately. The localized radial basis function neural networks learn the uncertainties cooperatively while tracking the period path, and the knowledge gained from learning is utilized to construct an empirically based formation control law using experience to cope with similar uncertainties rather than repeatedly using adaptive methods, which reduces the computing burden. The position of the leader is assumed to be available only for the leader's neighboring AUV, and a novel finite-time distributed observer is presented for the followers to estimate the leader's position. Based on this, the control law is derived from the prescribed performance control method using a finite-time performance function rather than exponential decaying function to enable the tracking error converges in finite time, which accelerates the learning process. The simulation results confirm the validity of the presented control protocol.

Neural network-based depth and horizontal control for autonomous underwater vehicles with prescribed performance

This paper presents a study of horizontal and depth controller design for autonomous underwater vehicles (AUVs) regarding model uncertainties, input constraints, and ocean currents. Firstly, the depth-plane and horizontal-plane models of AUV are established comprehensively with ocean currents. Besides, the AUV's characteristics and physical limitations are also described. Secondly, extended perturbation observers (EPOs) are innovatively constructed to estimate ocean currents, which guarantee the accuracy in the case of time-varying ocean currents. To achieve the position errors with prescribed performance guarantees, a finite-time prescribed performance function is proposed such that the convergence time, maximum overshoot, and steady error bounds can be preset directly. Neural network-based adaptive control is adopted to deal with model uncertainties and environmental disturbances. Auxiliary systems with a smooth switching function are introduced to alleviate the effect of input saturation. Furthermore, rigorous theoretical analyses are also carried out to demonstrate the robust stability of the proposed controllers. Finally, extensive numerical simulation studies confirm the strong and conclusive evidence for the proposed method's effectiveness, robustness, anti-jamming ability, and feasibility.

Error scaling–based adaptive region tracking control for autonomous underwater vehicles

This article investigates the region tracking control problem for an autonomous underwater vehicle subject to external disturbances, model uncertainty, and large initial deviation. In the existing region tracking control schemes, the control signals have a serious chattering phenomenon when autonomous underwater vehicle’s initial deviation is relatively large and the region tracking performance is only investigated from the position information. This article proposes an improved adaptive region tracking control scheme for autonomous underwater vehicle based on error scaling. Specifically, in the improved control scheme, the initial tracking error is artificially reduced by introducing an improved nonlinear mapping function to avoid the singular phenomenon and reduce the violent chattering phenomenon. In addition, the velocity error boundary is amplified by introducing the piecewise Lyapunov function to avoid the overadjustment of adaptive parameters in the developed controller. Meanwhile, it also reduces the frequent switching of control signals in the traditional control scheme which is caused by the pursuit of high-velocity precision. The region tracking performance is analyzed based on Lyapunov theory. Finally, the improved control scheme is applied to an autonomous underwater vehicle for simulation verification, and the comparison results demonstrate the effectiveness of the improved control scheme.

Optimal control of underwater vehicle using LQR controller driven by new matrix decision control algorithm

Optimal control of underwater vehicle using LQR controller driven by new matrix decision control algorithm

Kalypso Autonomous Underwater Vehicle: A 3D-Printed Underwater Vehicle for Inspection at Fisheries

Abstract In fish farms, a major issue is the net cage wear, resulting in fish escapes and negative impact on fish quality, due to holes and biofouling of the nets. To minimize fish losses, fisheries utilize divers to inspect net cages on a weekly basis. Aquaculture companies are looking for ways to maximize profit, and reducing maintenance costs is one of them. Kefalonia Fisheries spend 250,000 euros yearly on diver expenses for net cages maintenance. This work is about the design, fabrication, and control of an inexpensive autonomous underwater vehicle (AUV) intended for inspection in net cages at Kefalonia Fisheries S.A. in Greece. Its main body is 3D printed, and its eight-thruster configuration grants it six degrees-of-freedom. The main objective of the vehicle is to limit maintenance costs by increasing inspection frequency. The design, fabrication, electronic components, and software architecture of the AUV are presented. In addition, the forces affecting kalypso, mobility realization, navigation, and modeling are quoted along with a flow simulation and the experimental results. The proposed design is adaptable and durable while remaining cost-effective, and it can be used for both manual and automatic operations.

Model-Free Formation Control of Autonomous Underwater Vehicles: A Broad Learning-Based Solution

Model-Free Formation Control of Autonomous Underwater Vehicles: A Broad Learning-Based Solution

Command Filter-Based Adaptive Practical Prescribed-Time Asymptotic Tracking Control of Autonomous Underwater Vehicles With Limited Communication Angles

This work proposes a command filter-based adaptive practical prescribed-time (PPT) asymptotic tracking control scheme for autonomous underwater vehicles (AUVs) under dynamic uncertainties. Considering that the followers have limited communication angles in the leader–follower formation structure, where a novel controller is proposed to ensure communication angles to achieve preassignable precision within a predefined time. First, we present a PPT control for handling the limited communication angles and, thus, they are allowed to be limited, turnable, and controllable. Then, to achieve the asymptotic convergence of output tracking errors, the proposed adaptive controllers can effectively insure the trajectory tracking errors of AUVs asymptotically converge to zero under the influence of dynamic uncertainties. Furthermore, the derived asymptotically tracking command filter technique for AUVs not only deduce an acceleration-free strategy for followers but also achieve the asymptotic convergence of output tracking errors for the command filters themselves. In the end, the effectiveness of the proposed control strategy is demonstrated through stability analysis and simulation test.

Dynamics and Control of a Buoyancy-Driven Underwater Vehicle for Estimating and Tracking the Scattering Layer

This paper presents the design of a control strategy for an autonomous buoyancy-driven underwater vehicle, called the Driftcam, tasked with estimating and tracking the pelagic acoustic-scattering layer at depths of up to 700 meters. This reference-tracking control design is an alternative to the onboard control currently implemented on the Driftcam, as the existing control architecture is not designed for time-varying setpoint tracking. Tracking a known reference is presented using state-feedback control for continuous- and discrete-time command inputs. The known reference signal is chosen to represent the pelagic scattering layer vertical migration dynamics, modeled as a simple harmonic oscillator and estimated with a dynamic observer. We define a measurement function for the depth-varying density of organisms observable in the scattering layer. Time-varying reference tracking control is presented using output feedback for continuous- and discrete-time command inputs, where the reference is the online position estimate of the scattering layer. We also present an offboard trajectory motion planning algorithm using the existing onboard controller to emulate the response of the proposed reference tracking strategy. Experimental results from laboratory and field testing of the trajectory motion planner demonstrate that the buoyancy engine can feasibly achieve the desired behavior of the reference-tracking control strategy.

Robust Model Predictive Control Based on Active Disturbance Rejection Control for a Robotic Autonomous Underwater Vehicle

This work aims to develop a robust model predictive control (MPC) based on the active disturbance rejection control (ADRC) approach by using a discrete extended disturbance observer (ESO). The proposed technique uses the ADRC approach to lump disturbances and uncertainties into a total disturbance, which is estimated with a discrete ESO and rejected through feedback control. Thus, the effects of the disturbances are attenuated, and a model predictive control is designed based on a canonical model free of uncertainties and disturbances. The proposed control technique is tested through simulation into a robotic autonomous underwater vehicle (AUV). The AUV’s dynamic model is used to compare the performance of a classical MPC and the combined MPC-ADRC. The evaluation results show evidence of the superiority of the MPC-ADRC over the classical MPC under tests of reference tracking, external disturbances rejection, and model uncertainties attenuation.