Autonomous Underwater Vehicle Tracking Research Articles

Quaternion-based fixed-time tracking control for an autonomous underwater vehicle with consideration of measurement bias

This article investigates trajectory and attitude tracking control problem of a 6-degree of freedom (DOF) autonomous underwater vehicle (AUV) subject to measurement bias and external disturbance. First, the model dynamics of rotational channels are established by unit-quaternion, which possesses simplicity and broader applicability compared to the Euler angle such that the maneuver signal could be uniquely determined. Meanwhile, by introducing constraint of the logarithm function and initial value, the unwinding phenomenon existing in rotation motion can be avoided tactfully. Considering the high requirement for sensor accuracy, the resulting measurement bias in position and velocity channels, making AUV into a mismatched system and bring the uncertain term in the process of controller design. Therefore, the echo state network (ESN) with online updates performance is applicable to approximate uncertain term simultaneously generated by measurement bias and unknown external disturbance. Then, by incorporating composite hyperbolic tangent function, the quasi-fast terminal sliding mode (QTSM) surface is constructed to realize the fixed-time convergence peculiarity of the AUV tracking system. Compared with the existing methods, the proposed algorithm can regulate artificially the stable schedule of the AUV tracking system and possess the capacity of bias tolerance and anti-disturbance. Finally, simulation results verify the validity and feasibility of the developed control algorithm.

Improved Adaptive High-Order Sliding Mode-Based Control for Trajectory Tracking of Autonomous Underwater Vehicles

Improved Adaptive High-Order Sliding Mode-Based Control for Trajectory Tracking of Autonomous Underwater Vehicles

Autonomous Underwater Vehicle (AUV) Motion Design: Integrated Path Planning and Trajectory Tracking Based on Model Predictive Control (MPC)

This paper attempts to develop a unified model predictive control (MPC) method for integrated path planning and trajectory tracking of autonomous underwater vehicles (AUVs). To deal with the computational burden of online path planning, an event-triggered model predictive control (EMPC) method is introduced by using the environmental change as a triggering mechanism. A collision hazard function utilizing the changing rate of hazard as a triggering threshold is proposed to guarantee safety. We further give an illustration of how to calculate this threshold. Then, a Lyapunov-based model predictive control (LMPC) framework is developed for the AUV to solve the trajectory tracking problem. Leveraging a nonlinear integral sliding mode control strategy, we construct the contraction constraint within the formulated LMPC framework, thereby theoretically ensuring closed-loop stability. We derive the necessary and sufficient conditions for recursive feasibility, which are subsequently used to prove the closed-loop stability of the system. In the simulations, the proposed path planning and tracking control are verified separately and integrated and combined with static and dynamic obstacles.

Adaptive tracking control of underactuated AUV with historical navigation information and piecewise weighted fractional order integration

In this study, a piecewise adaptive weighted fractional order integration (PAWFOI) is introduced in cascade-backstepping control, which improves the effectiveness of autonomous underwater vehicles (AUVs) in suppressing navigation errors during long-duration operations. Additionally, PAWFOI significantly reduces the computational complexity compared to conventional fractional order integration, ensuring a consistent computational speed. The adaptive weight selector dynamically adjusts the weights based on the AUV state information, achieving enhanced tracking performance. Based on Lyapunov’s theory, the results demonstrated that AUV tracking control can be achieved by selecting appropriate control parameters. Finally, an example is presented to verify the correctness and validity of the theoretical results.

Full prescribed performance trajectory tracking control strategy of autonomous underwater vehicle with disturbance observer

This paper investigates trajectory tracking control of the Autonomous Underwater Vehicle (AUV) with the general uncertainty consisting of model uncertainties and unknown ocean current disturbances. A full prescribed performance control strategy based on disturbance observer is developed, which ensures that the tracking error, the velocity error, and the observation error are all constrained. First, under the case of unmeasurable AUV acceleration, a fixed-time observer is constructed to estimate the general uncertainty, which constrains the observation error within the prescribed accuracy by a prescribed performance observer. Then, based on the performance function and corresponding error transformation, a prescribed performance protocol is designed to realize the trajectory tracking control, so that the observation error, the tracking error, and the velocity error are all constrained within the prescribed accuracy range. Simulation results demonstrate the efficiency of the full prescribed performance control strategy while the AUV tracking control with full state constraints is feasible. Moreover, compared with the other two relevant works, this study improves the observation performance by at least 10 %, both in case of deepwater disturbances and near-surface disturbances.

Three-Dimensional Path Tracking of Over-Actuated AUVs Based on MPC and Variable Universe S-Plane Algorithms

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.

Deep learning-based robust positioning scheme for imaging sonar guided dynamic docking of autonomous underwater vehicle

In this paper, a deep learning-based underwater positioning scheme is proposed to achieve robust feature tracking of an autonomous underwater vehicle (AUV) in sonar image during dynamic docking. To address the issues that the distorted feature and acoustic noises lead significant difficulty to detection and tracking of AUV in acoustic image during dynamic docking, first, a pre-trained You Only Look Once (YOLO) network is applied to detect both body and head features of AUV. Second, we introduce an Intersection Over Union (IOU) match-based backend which preliminarily filters the error detections of AUV head based on the rigid relationship between body and head of AUV. Subsequently, Simple Online and Realtime Tracking with a deep association metric (DeepSort) is utilized to achieve track matching of all detection results including error detections and real target. Moreover, a scoring mechanism is presented to further remove the unfiltered error detections based on the motion tendency of detection tracks. Experiment result shows that the proposed scheme enables real-time and robust feature tracking of AUV with the interference of feature distortion, reverberation and environmental noises.

Rotation matrix-based finite-time trajectory tracking control of AUV with output constraints and input quantization

This study investigated a rotation-matrix-based finite-time trajectory tracking problem for autonomous underwater vehicles (AUVs) in the presence of output constraints, input quantization, and uncertainties. First, a rotation matrix-based attitude representation is introduced, which allow attitude dynamics to be globally and uniquely represented without unwinding. To satisfy the finite-time stability of AUV tracking control and the output constraints imposed by introducing the new attitude error vector, a novel finite-time command-filtered backstepping controller (FTCFBC) is proposed based on the asymmetrical time-varying barrier Lyapunov function (TVBLF). Subsequently, a second-order auxiliary dynamic system (ADS) is proposed to estimate the negative effects that of input quantization errors. Because the quantized control inputs can be switched at an appropriate earlier or later switching timing depending on the output of the ADS, the negative impact of quantization errors on the control accuracy is reduced. Moreover, an adaptive finite-time disturbance observer (AFTDO) is developed to estimate the lumped uncertainties without prior information on the bounds of the uncertainties. Finally, the results of the theoretical analysis verified that the tracking errors can converge within a finite time. Numerical simulations confirmed the effectiveness of the proposed control scheme.

Observer-Based Adaptive Control for Trajectory Tracking of AUVs with Input Saturation

In this paper, an observer-based adaptive control method is investigated for the horizontal trajectory tracking of autonomous underwater vehicles (AUV) with input saturation and system disturbances. Firstly, the desired surge speed and trajectory angle are established, which could decouple the tracking error subsystem and avoid the complex form. Secondly, the input saturation is approximated by a smooth function, and a nonlinear extended states observer (NESO) is designed for estimating system disturbances. Based on the command filtered backstepping technique, which can avoid the explosion caused by the derivative of the virtual control, an observer-based adaptive output feedback control method is developed, and an auxiliary system is applied to compensate for filtered tracking errors, input saturation bias, and observer errors. Finally, simulation results show the proposed method has good robustness in the face of system uncertainties, and the error is nearly 33.3% smaller than that of other control methods when meeting sudden trajectory changes. A good control performance is guaranteed.

Disturbance suppression and NN compensation based trajectory tracking of underactuated AUV

For the trajectory tracking of underactuated autonomous underwater vehicle (AUV) subject to disturbances, a disturbance suppression and neural network (NN) compensation based control strategy is proposed. Lyapunov method is used to guide the overall design of control system. To deal with the underactuation, Light of sight (LOS) guidance is used to establish the relationship between heading angle and cross-track error. To suppress the external disturbance, L2-gain design is employed to guarantee the controller robustness. To improve the tracking accuracy, on-line neural networks with guaranteed stability are designed to identify unknown dynamics including the derivatives of virtual controls and errors induced by input saturation. Numerical simulation is performed to verify the effectiveness of the proposed control strategy. Compared with sliding mode controller (SMC) and disturbance observer approaches, the proposed controller performs better in terms of robustness and the input saturation is alleviated.

Trajectory tracking of autonomous underwater vehicle under disturbance based on time-delay adaptive high-order sliding mode control

This paper introduces an innovative adaptive generalized super twisting algorithm (AGSTA) controller combined with a time-delay estimator (TDE) to address the trajectory-tracking issue in autonomous underwater vehicles (AUVs) impacted by disturbances. AUVs utilized in underwater exploration often carry sensors, such as Doppler velocity logs, for position and speed determination. However, owing to instrument precision limitations and other factors, these sensors suffer from a slow sampling speed. Furthermore, manual tuning requirement, conventional GSTAs do not provide substantial benefits in real-world applications, adversely affecting AUV position control. To rectify this issue, we proposed a TDE founded on delayed sensor data to estimate the vehicle fluid dynamics, integrated with an AGSTA, to enhance the controller performance. A comprehensive stability analysis of the control system was carried out under these parameters. The effectiveness of the proposed method was demonstrated through simulation results.

Hybrid Layer of Improved Interfered Fluid Dynamic System and Nonlinear Model Predictive Control for Navigation and Control of Autonomous Underwater Vehicles

This study introduces a hybrid control structure called Improved Interfered Fluid Dynamic System Nonlinear Model Predictive Control (IIFDS-NMPC) for the path planning and trajectory tracking of autonomous underwater vehicles (AUVs). The system consists of two layers; the upper layer utilizes the Improved Interfered Fluid Dynamic System (IIFDS) for path planning, while the lower layer employs Nonlinear Model Predictive Control (NMPC) for trajectory tracking. Extensive simulation experiments are conducted to determine optimal parameters for both static and dynamic obstacle scenarios. Additionally, real-world testing is performed using the BlueRov2 platform, incorporating multiple dynamic and static obstacles. The proposed approach achieves real-time control at a frequency of 100 Hz and exhibits impressive path tracking accuracy, with a root mean square (RMS) of 0.02 m. This research provides a valuable framework for navigation and control in practical applications.

Distributed Dual Closed-Loop Model Predictive Formation Control for Collision-Free Multi-AUV System Subject to Compound Disturbances

This paper focuses on the collision-free formation tracking of autonomous underwater vehicles (AUVs) with compound disturbances in complex ocean environments. We propose a novel finite-time extended state observer (FTESO)-based distributed dual closed-loop model predictive control scheme. Initially, a fast FTESO is designed to accurately estimate both model uncertainties and external disturbances for each subsystem. Subsequently, the outer-loop and inner-loop formation controllers are developed by integrating disturbance compensation with distributed model predictive control (DMPC) theory. With full consideration of the input and state constraints, we resolve the local information-based DMPC optimization problem to obtain the control inputs for each AUV, thereby preventing actuator saturation and collisions among AUVs. Moreover, to mitigate the increased computation caused by the control structure, the Laguerre orthogonal function is applied to alleviate the computational burden in time intervals. We also demonstrate the stability of the closed-loop system by applying the terminal state constraint. Finally, based on a connected directed topology, comparative simulations are performed under various control schemes to verify the robustness and superior performance of the proposed scheme.

An augmented state Gaussian mixture probability hypothesis density filter for multitarget tracking of autonomous underwater vehicles

Multitarget tracking is an important task for autonomous underwater vehicles (AUVs). Due to the limited space on AUVs, the aperture of the sonar array mounted on an AUV is small. The finite resolution of AUV sonar systems results in weak targets being masked by strong targets when they are close in angular space, leading to the trajectory fracture problem of weak targets in the azimuth crossing scenario. To address this problem, an augmented state Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed. Since the acoustic signals radiated by targets contain some narrowband discrete components (known simply as tonals or lines), we focus on leveraging the tonals to enhance tracking performance. First, we model the augmented state dynamics, which contain both direction-of-arrival (DOA) and tonal components, and DOA tracks can be generated by the augmented state GM-PHD filter. Second, track segments are clustered by the modified density-based spatial clustering of applications with noise (DBSCAN) algorithm according to the estimated tonals. Finally, track segments with the same clustering results are associated with the same target, and the track stitching method is applied to overcome the trajectory fracture problem. The effectiveness of the algorithm is verified by simulation experiments in different scenarios.

Robust MPC-based trajectory tracking of autonomous underwater vehicles with model uncertainty

A robust model predictive control (MPC) method with dual closed-loops is presented to handle trajectory tracking of autonomous underwater vehicle (AUV) with uncertain model parameters and random external perturbations. First, constraint conditions are set for the motion state and control input of the underwater vehicle based on its motion characteristics. The position controller takes the velocity increment as input, thus providing a smoothly varying desired velocity for the velocity controller. The velocity controller comprises nominal MPC and a nonlinear auxiliary control law to overcome the effect of random perturbations on AUV tracking control. Then, a finite-time extended state observer (FTESO) is designed to compensate for dynamic model uncertainty. Furthermore, Lyapunov stability theory is employed to analyse the stability of the controller and FTESO. Ultimately, through comparative simulation experiments, the proposed control framework's effectiveness and robustness are verified, proving it to be a feasible AUV trajectory tracking control method.

A Disturbance Observer-based Novel Fuzzy SMC Control Scheme for Path Tracking of AUV with Mismatched Disturbances

A new sliding mode control scheme is proposed for the path tracking problem of autonomous underwater vehicles (AUVs) with uncertainty and mismatch interference. Considering the complexity of parameter rectification, a fuzzy logic controller-based system rectification scheme is designed in this paper. In the control system, a disturbance observer (DOB) and a radial basis function neural network (RBFNN) are used to estimate the mismatch disturbance and uncertainty, respectively. In addition, this paper analyzes the conditions for the Liapunov stability of the designed controller and the results show the closed-loop stability of the control system. The control scheme can be applied to the path tracking of underwater robots with rapidly changing trajectories in different driving environments. Finally, a number of comparative experiments are carried out to demonstrate the good performance of the proposed controller.

Active fault tolerant control based on compound iterative learning observer for trajectory tracking of autonomous underwater vehicles

This article proposes a novel active non-singular integral terminal sliding mode fault tolerant control scheme based on compound iterative learning observer (CILO-NITSM-FTC) for the trajectory tracking control problems of autonomous underwater vehicles (AUVs), which accompanied by nonlinear dynamics and possible thruster failures. The designed compound iterative learning observer (CILO) can effectively estimate the values of nonlinear dynamics and reconstruct the faults of the thrusters. Compared to the existing adaptive non-singular integral terminal sliding mode-based fault tolerant control (ANITSM-FTC), which is a passive fault tolerant control method essentially, the mentioned CILO-NITSM-FTC in this paper presents faster rate of convergence and more accurate control precision. In the design process of CILO, radial basis function neural network (RBFNN) is adopted to estimate the nonlinear dynamics, then approximation errors of neural network, thruster faults and external disturbances are considered as global disturbances. The estimated values obtained from CILO can be used to perform feedforward compensation on the designed controller to complete the reconstruction of thruster faults. Through rigorous mathematical proof and comparative numerical simulation experiments, the superiority and practicability of CILO-NITSM-FTC proposed in this paper are verified.

Research on the Control Problem of Autonomous Underwater Vehicles Based on Strongly Coupled Radial Basis Function Conditions

This paper addresses tracking control problems for autonomous underwater vehicle (AUV) systems with coupled nonlinear functions. For the first time, the radial basis function (RBF) is applied to the model reference adaptive control system, and the vehicle horizontal plane model is proposed. When the AUV movement is affected by the driving force, ocean resistance, and the force generated by the water current, the expected output of the AUV’s system is difficult to meet the expectations, making the AUV trajectory tracking problems challenging. There are two main options for finding suitable controllers for AUVs. The first is making the AUV model achieve better stability using a more complex controller. The second is the simpler controller structure, which can ensure faster system feedback. The RBF and model reference adaptive control (MEAC) system are combined to increase the number of hidden layers, increasing the AUV tracking stability. Because the embedded computing module of an AUV is a bit limited, 31 hidden layers are chosen to simplify the controller structures. A couple of Lyapunov functions are designed for the expected surge and sway velocities, and the vehicle tracking error gradually converges to (0,0). The controller design results are imported into the AUV actuator model by software, and after 0.64 s, the AUV tracking error is less than 1%. At last, the vehicle tracking experiments were carried out, showing that after 0.5 s, the AUV tracking error was less than 1%.

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.

A Bio-Inspired MEMS Wake Detector for AUV Tracking and Coordinated Formation

AUV (Autonomous Underwater Vehicle) coordinated formation can expand the detection range, improve detection efficiency, and complete complex tasks, which requires each AUV to have the ability to track and locate. A wake detector provides a new technical approach for AUV cooperative formation warfare. Now, most of the existing artificial lateral line detectors are for one-dimensional flow field applications, which are difficult to use for wake detection of AUVs. Therefore, based on the pressure gradient sensing mechanism of the canal neuromasts, we apply Micro-Electro-Mechanical System (MEMS) technology to develop a lateral line-inspired MEMS wake detector. The sensing mechanism, design, and fabrication are demonstrated in detail. Experimental results show the detector’s sensitivity is 147 mV·(m/s)−1, and the detection threshold is 0.3 m/s. In addition, the vector test results verify it has vector-detecting capacity. This wake detector can serve AUVs wake detection and tracking technology, which will be promising in AUV positioning and coordinated formation.