Problem Of Autonomous Underwater Vehicle Research Articles

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.

Adaptive Cooperative Formation Control of Autonomous Underwater Vehicles Based on Event-Triggered Mechanism

This paper investigates the coordinated motion control problem of autonomous underwater vehicles (AUVs), and an adaptive cooperative formation control method based on event-triggered mechanism is designed. In the estimation of external disturbance and model uncertainty, adaptive parameters are adopted to represent the upper bound of the learning weight matrix, which reduces the computational pressure of the system. It is proved theoretically that the signals in the system are semi-global uniformly ultimately bounded and independent of the initial state of the system. An event-triggered control mechanism is introduced, and the system updates the control law only when the event is triggered. Simulation results show that the controller can achieve the expected formation shape and save the communication resources of the system.

Robust adaptive path following control of autonomous underwater vehicle with uncertainties and communication bandwidth limitation

This article investigates the path-following control problem for autonomous underwater vehicles (AUVs) subject to external disturbances and unknown model parameters. An event-triggered model-free adaptive control (ETMFAC) method is designed with a practical experimental platform. Firstly, a line-of-sight (LOS) guidance law is employed to obtain the desired heading angle for the AUV. Secondly, an improved model-free adaptive control method is used, in which an adaptive law is introduced to compensate for the effects caused by external disturbances. Subsequently, an event-triggering mechanism that can reduce the communication and calculation burden of the control system is proposed. As for the state mutation caused by variable disturbances, an adaptive triggering threshold is designed based on the state vector of the AUV to maintain satisfactory following performance. The stability of the control system is proved by theoretical analysis. Finally, both numerical simulations and practical experiments are conducted to evaluate the efficacy and prove the superiority of the proposed method.

Neural Adaptive Quantitative Prescribed Performance Sectionalized Event-Triggered Control for Autonomous Underwater Vehicles

In this paper, we study the quantitative design paradigm (QDP) of tracing control for a class of second-order system and further extend this to solve the trajectory tracking problem of autonomous underwater vehicles. The key merit of QDP is the capability of assigning some quantitative indices (e.g., overshoot and convergence time). To pursue performance enhancement with regard to tracking performance and bandwidth saving, a sectionalized event-triggered mechanism (SETM) incorporated with prespecified convergence time is proposed. To recognize the peculiarities of the lumped disturbances, an estimation-triggered neural network (ETNN) is designed via a property indicator, such that the disturbances that deteriorate the performance of the closed-loop system will be compensated and beneficial disturbances will be reserved otherwise, enabling less energy consumption without sacrificing the tracking performance. Theoretical analysis and simulation results are provided and analyzed, validating the performance and efficiency of the proposed solution.

Adaptive event-triggered coordination control of unknown autonomous underwater vehicles under communication link faults

Coordination control of autonomous underwater vehicles finds broad applications in various marine activities. This paper focuses on the adaptive event-triggered coordination control problem of autonomous underwater vehicles under the effects of nonlinear and coupled dynamics, unknown system parameters, and communication link faults. The limited communication capacity is considered and an event-triggered distributed observer is designed under the effects of communication link faults. Reinforcement learning method is implemented to learn optimal coordination controller to achieve the position formation and the attitude synchronization for autonomous underwater vehicles with unknown dynamics. Simulation results verify the effectiveness of the proposed approach.

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.

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%.

Event-triggered model-parameter-free trajectory tracking control for autonomous underwater vehicles

This article focuses on the event-triggered model-parameter-free (ETMPF) control problem for autonomous underwater vehicles (AUVs). To realize the trajectory tracking goal under internal model parameter uncertainties and external disturbances, the ETMPF control law is firstly presented via the properties of Euler–Lagrange systems. In the designed ETMPF control law, a static event-triggered mechanism (SETM) is developed to reduce its communication burden with actuators. Then, by introducing internal dynamic variables for the SETM, a dynamic event-triggered mechanism (DETM) is further developed. Compared to the SETM, the maximum triggering frequency under the DETM is computable and can be adjustable by tuning control parameters, which makes it applicable to more serve network resource-limited scenarios. Finally, the effectiveness of the designed two ETMPF control schemes is demonstrated through theoretical analysis and simulation results.

Homing tracking control of autonomous underwater vehicle based on adaptive integral event-triggered nonlinear model predictive control

The homing problem of autonomous underwater vehicle with constraints and bounded disturbances is studied in this paper. More specifically, a nonlinear model predictive control (NMPC) framework based on adaptive integral event-triggered (AIET) mechanism is designed for AUV tracking mobile docking station (DS). Firstly, according to the characteristics of AUV homing, a guidance system based on mobile DS is designed, which can provide a feasible reference trajectory for the subsequent AUV tracking independent of the vehicle model. Then, in the framework of AIET-NMPC control, the designed tracking controller can reduce the computational cost while guaranteeing the control accuracy. The proposed AIET mechanism can dynamically adjust the trigger threshold according to the integral error between the real state and the optimal state of the system. In addition, by designing exponential robust constraints in the NMPC optimization problem, the system stability margin is increased. Finally, it strictly proves the Zeno free behavior, the feasibility of the algorithm, and the stability of the system. Simulation results demonstrate that the proposed control approach yields better effects in terms of tracking control performance and computational performance compared to other methods.

Trajectory Planning of Autonomous Underwater Vehicles Based on Gauss Pseudospectral Method.

This paper aims to address the obstacle avoidance problem of autonomous underwater vehicles (AUVs) in complex environments by proposing a trajectory planning method based on the Gauss pseudospectral method (GPM). According to the kinematics and dynamics constraints, and the obstacle avoidance requirement in AUV navigation, a multi-constraint trajectory planning model is established. The model takes energy consumption and sailing time as optimization objectives. The optimal control problem is transformed into a nonlinear programming problem by the GPM. The trajectory satisfying the optimization objective can be obtained by solving the problem with a sequential quadratic programming (SQP) algorithm. For the optimization of calculation parameters, the cubic spline interpolation method is proposed to generate initial value. Finally, through comparison with the linear fitting method, the rapidity of the solution of the cubic spline interpolation method is verified. The simulation results show that the cubic spline interpolation method improves the operation performance by 49.35% compared with the linear fitting method, which verifies the effectiveness of the cubic spline interpolation method in solving the optimal control problem.

A finite-time adaptive sliding mode control based on DOB for AUVs subject to matched and mismatched disturbances

This paper proposes an adaptive sliding mode control (SMC) based on disturbance observer (DOB) for trajectory tracking problem of autonomous underwater vehicles (AUVs). Due to sensitivity of the SMC to mismatched disturbances, this paper presents an adaptive SMC based on a new exponential reaching law. The controller uses a DOB to estimate both matched and mismatched time-varying disturbances. The stability of DOB in the presence of time-varying disturbances is proved via the Lyapunov stability theorem. To design the controller, sliding surfaces containing estimation of the matched and mismatched disturbances are defined. In order to increase the speed of reaching to the surfaces and avoid chattering, the gains of the proposed reaching law have been adapted based on the sliding surfaces. It has been proved that the proposed reaching law converges to the sliding surfaces in a finite time. The stability of the overall system with DOB and controller has also been proved using the Lyapunov technique. A simulation study has conducted to test how the proposed method controls the AUVs to track a desired trajectory when the vehicles are exposed to both matched and mismatched disturbances. The results demonstrate that the performance of the proposed controller is superior to the controllers previously reported in the literature.

Adaptive prescribed performance second-order sliding mode tracking control of autonomous underwater vehicle using neural network-based disturbance observer

For trajectory tracking control problem of autonomous underwater vehicle (AUV) with model uncertainties, unknown nonlinear external disturbances, and input saturation, an observer-based adaptive second-order sliding mode control scheme with prescribed performance is proposed. Firstly, a finite time performance function (FTPF) is constructed to constrain the tracking error to the prescribed precision within the preset finite time, and achieve the prescribed dynamic convergence performance and tracking accuracy. Then, a neural network-based disturbance observer (NNDO) is designed to deal with model uncertainties and external disturbances, respectively. Based on prescribed performance control and sliding mode control technique, an adaptive prescribed performance second-order sliding mode control strategy was proposed. In addition, we construct an auxiliary system to overcome the effect of thrust saturation. Lyapunov method is applied to demonstrate the stability of the closed-loop system. Finally, the validity of the proposed control law is verified by numerical simulations.

AUV Formation Coordination Control Based on Transformed Topology under Time-Varying Delay and Communication Interruption

Aiming at the control problem of autonomous underwater vehicle (AUV) pilot-following formation with communication delay and communication interruption, a controller based on feedback linearization and the PD control method is designed in this paper. Firstly, the nonlinear, strongly coupled vehicle model is transformed into a second-order model via the feedback linearization method, and then the formation coordination controller is designed based on consistency theory and the PD control method. The Markov random jump process is used to simulate the formation topology in the event of communication interruption. The condition of stable convergence of the AUV pilot-following formation is analyzed in the presence of time-varying delay and Markov transformation topology. A Lyapunov–Krasovskii equation is established, and linear matrix inequality (LMI) is used to solve the problem of communication interruption and communication delay. The boundary conditions of error convergence of the control system are obtained. Finally, the effectiveness of the formation coordination controller based on the second-order integral model under the unstable conditions of underwater acoustic communication is verified by simulation.

Fixed-time sliding mode output feedback tracking control for autonomous underwater vehicle with prescribed performance constraint

This paper studies the trajectory tracking control problem for autonomous underwater vehicles (AUVs) subject to external disturbances and model uncertainties. To improve the tracking performance of the AUVs, a prescribed performance function is introduced to constrain the output tracking error. Then, a fixed-time extended state observer (FxTESO) is set up to estimate the unknown states and the lumped disturbances (including external disturbances and model uncertainties). With the outputs of the FxTESO, a novel dynamic fixed-time terminal sliding mode (FxTTSM) surface is constructed and then a fixed-time output feedback controller is designed to achieve trajectory tracking control for the AUVs. By employing Lyapunov function theory, the stability of the closed-loop system is analyzed. Simulation results are provided to verify the effectiveness of the developed control scheme.

Experimental Validation of a Model-Free High-Order Sliding Mode Controller with Finite-Time Convergence for Trajectory Tracking of Autonomous Underwater Vehicles

Several control strategies have been proposed for the trajectory tracking problem of Autonomous Underwater Vehicles (AUV). Most of them are model-based, hence, detailed knowledge of the parameters of the robot is needed. Few works consider a finite-time convergence in their controllers, which offers strong robustness and fast convergence compared with asymptotic or exponential solutions. Those finite-time controllers do not permit the users to predefine the convergence time, which can be useful for a more efficient use of the robot’s energy. This paper presents the experimental validation of a model-free high-order Sliding Mode Controller (SMC) with finite-time convergence in a predefined time. The convergence time is introduced by the simple change of a time-base parameter. The aim is to validate the controller so it can be implemented for cooperative missions where the communication is limited or null. Results showed that the proposed controller can drive the robot to the desired depth and heading trajectories in the predefined time for all the cases, reducing the error by up to 75% and 41% when compared with a PID and the same SMC with asymptotic convergence. The energy consumption was reduced 35% and 50% when compared with those same controllers.

Data-driven trajectory tracking control for autonomous underwater vehicle based on iterative extended state observer.

In this study, we explore the precise trajectory tracking control problem of autonomous underwater vehicle (AUV) under the disturbance of the underwater environment. First, a model-free adaptive control (MFAC) is designed based on data-driven ideology and a full-form dynamic linearization (FFDL) method is utilized to online estimate time-varying parameter pseudo gradient (PG) to establish an equivalent data model of AUV motion. Second, the iterative extended state observer (IESO) scheme is designed to combine with FFDL-MFAC. Because the proposed novel controller is able to learn from repeated iterations, the proposed novel controller can estimate and compensate the model approximation error produced by external environmental unknown disturbance. Third, three-dimensional motion is decoupled into horizontal and vertical and a multi closed-loop control structure is designed that exhibits faster convergence rate and reduces sensitivity to parameter jumps than single closed-loop system. Finally, two simulation scenarios are designed featuring non external disturbance and Gaussian noise of signal-to-noise ratio of 90 dB. The simulation results reveal the superiority of FFDL. Furthermore, we adpot the technical parameters data of T-SEA I AUV to conduct numerical simulation, aunderwater trajectory as the tracking scenario and set waves to 0.5 m and current to 0.2 m/s to simulate Lv.2 ocean conditions of "International Ocean State Standard". The simulation results demonstrate the effectiveness and robustness of the proposed tracking control algorithm.

Fault feature extraction and fusion method for AUV with weak thruster fault based on variational mode decomposition and D-S evidence theory.

This study investigated the fault feature extraction and fusion problem for autonomous underwater vehicles with weak thruster faults. The conventional fault feature extraction and fusion method is effective when thruster faults are serious. However, for a weak thruster fault, that is, when the loss of effectiveness of thrusters is less than 10%, the following two problems occur if the conventional method is used. First, the ratio of fault features to noise features is small. Second, there is no monotonic relationship between the fusion fault features fused by the conventional method and the fault severity. In this paper, the following two methods are proposed to solve this problem: 1) Fault-feature extraction method. Based on negentropy, this method improves the evaluation index of the parameter optimization of the modified variational mode decomposition and finally enhances the fault features extracted by the modified Bayesian classification algorithm. 2) Fault-feature fusion method. To create a monotonic relationship between the fusion fault features and fault severity, this method expands the number of original signals of the traditional fusion method based on D-S evidence theory, improves the focus element of the traditional fusion method, and adopts the strategy of double fusion. Finally, the effectiveness of the proposed method was verified by pool-experiment results on Beaver II prototype.

Event-triggered adaptive fuzzy fault-tolerant control for autonomous underwater vehicles with prescribed tracking performance

In this paper, we solve the trajectory tracking control problem of autonomous underwater vehicles (AUVs) with actuator faults and model uncertainties. In order to achieve the output tracking with prescribed transient performance, we develop a novel tan-type barrier Lyapunov function (BLF)-based funnel tracking control strategy to constrain the position tracking error of the AUV within prespecified performance funnels. Furthermore, to maintain the predesigned transient tracking performance of the AUV when the actuator fault occurs, an event-triggered adaptive fuzzy fault-tolerant control (FTC) scheme is proposed to compensate actuator faults. Meanwhile, to handle the uncertainties caused by the system model, fuzzy logic systems (FLSs) are adopted to approximate the unknown hydrodynamic parameters and actuator fault parameters. Different from the existing results, the communication burden and the excess wear from the controller to the actuators are reduced utilising the event-triggered mechanism. By using the theory of Lyapunov stability, it is proven that all the signals of the closed-loop system are semiglobally uniformly bounded. Finally, the effectiveness of the proposed method is demonstrated by a simulation example.

Fixed-time sliding mode tracking control for autonomous underwater vehicles

In this paper, a fixed-time sliding mode controller (FSMC) with disturbance observer (DO) is proposed to solve the trajectory tracking control problem for autonomous underwater vehicles (AUVs) under time-varying external disturbances. Firstly, by using the sliding mode control method, an FSMC is proposed for reducing the tracking errors. Secondly, considering the estimation problem of time-varying disturbances, a fixed-time DO is designed to reconstruct the information of the AUV. Finally, The DO can compensate time-varying disturbances and the FSMC can reduce the tracking errors incorporating the information from the DO. Thus, the FSMC-DO for AUV is derived. Compared with the conventional FSMC and the finite time controller, the proposed FSMC-DO not only offers a faster convergence rate but also a higher tracking accuracy and robustness for the trajectory tracking control of AUVs. Simulation results are provided to show the superior performance of the proposed FSMC-DO.

Fixed‐time extended state observer‐based trajectory tracking control for autonomous underwater vehicles

AbstractIn this paper, we investigate the trajectory tracking control problem for autonomous underwater vehicles (AUVs) with time‐varying external disturbances of currents and waves in the sea. To solve the problem, a fixed‐time extended state observer (FTESO)‐based tracking controller is proposed. Firstly, by using the extended state observer, the FTESO is proposed for estimating the external disturbances and the velocities of the AUV within the fixed convergence time. Based on the FTESO, the fixed‐time controller is proposed for reducing the tracking errors and improving robustness performance. Theoretical analysis is given, which shows that the unknown external disturbances can be compensated and tracking error can converge to zero within the fixed time. Simulation results show that the proposed FTESO controller not only guarantees the fixed‐time convergence but also reduces the trajectory tracking errors of the AUV.