Model Of Underwater Vehicle Research Articles

Optimal input design for hydrodynamic derivatives estimation of nonlinear dynamic model of AUV

Input design has a dominant role in developing the dynamic model of an autonomous underwater vehicle (AUV) through system identification. Optimal input design is the process of generating informative inputs that can be used to provide a good-quality dynamic model of AUV. In this paper, amplitude-modulated pseudo-random binary signal (APRBS) inputs are optimally designed in order to estimate the hydrodynamic derivatives of an AUV’s nonlinear dynamic model. The input controls are designed so as to minimize uncertainty in estimating hydrodynamic derivatives. The employed approach can design multiple inputs and apply constraints on an AUV system’s inputs and outputs. The genetic algorithm is utilized to solve the constraint optimization problem. The presented algorithm is used for designing the input signals of Hydrolab300 AUV, and the estimation obtained by these inputs is compared with that of zigzag maneuver. According to the results, the designed APRBS inputs improve the uncertainties that exist in estimating hydrodynamic derivatives better than zigzag inputs.

Underwater terrain-aided navigation system based on combination matching algorithm

Considering that the terrain-aided navigation (TAN) system based on iterated closest contour point (ICCP) algorithm diverges easily when the indicative track of strapdown inertial navigation system (SINS) is large, Kalman filter is adopted in the traditional ICCP algorithm, difference between matching result and SINS output is used as the measurement of Kalman filter, then the cumulative error of the SINS is corrected in time by filter feedback correction, and the indicative track used in ICCP is improved. The mathematic model of the autonomous underwater vehicle (AUV) integrated into the navigation system and the observation model of TAN is built. Proper matching point number is designated by comparing the simulation results of matching time and matching precision. Simulation experiments are carried out according to the ICCP algorithm and the mathematic model. It can be concluded from the simulation experiments that the navigation accuracy and stability are improved with the proposed combinational algorithm in case that proper matching point number is engaged. It will be shown that the integrated navigation system is effective in prohibiting the divergence of the indicative track and can meet the requirements of underwater, long-term and high precision of the navigation system for autonomous underwater vehicles.

Low-level control technology of micro autonomous underwater vehicle based on intelligent computing

Based on the modeling of a micro autonomous underwater vehicle, an improved control structure and underlying control method are proposed for some parameters such as the automatic orientation, automatic depth, height, and speed of the micro autonomous underwater vehicle. A cascade double closed-loop control structure is proposed to control the horizontal plane by controlling properties such as the automatic depth, height, positioning, the response speed and adjustment precision of the control are improved. The parameters of the proportional-integral-derivative (PID) control method can be optimized by using particle swarm optimization (PSO), and the fuzzy controller is designed to compare with the PID control of the autonomous underwater vehicles. Compared with the traditional PID control, the control effect of PSO–PID controller is stronger than that of the tranditional PID controller. Due to the uncertainty of the micro autonomous underwater vehicle mathematical model, the position control of PID controller is weaker than the fuzzy controller. The simulation results show that the proposed method has fast dynamic response and acceptable robustness.

Adaptive fuzzy exponential terminal sliding mode controller design for nonlinear trajectory tracking control of autonomous underwater vehicle

This paper addresses an adaptive fuzzy exponential terminal sliding mode trajectory tracking control design for autonomous underwater vehicle (AUV) using time delay estimation. The proposed control scheme assures quick convergence due to a nonlinear expression in to an integral augmented sliding surface results in nonlinear sliding mode termed as integral-fast terminal sliding mode control (IFTSMC). Furthermore, control law requires no prior knowledge about highly nonlinear underwater vehicle model due to the time delay estimation method. Meanwhile, fuzzy logic system incorporated as adaptation mechanism afford for varying switching function and adaptive exponential reaching law utilized for faster reaching time in path tracking control performance. Finally, the effectiveness and robustness of the proposed control strategy are demonstrated through numerical simulations with Cyclops AUV in presence of parameter perturbations and unidentified disturbances like ocean current, wave and measurement noise.

Dynamics modeling and performance analysis of underwater vehicles based on the Boltzmann-Hamel equations approach

The paper addresses dynamics modeling of underwater vehicles, which are typical examples of variable mass and configuration mechanical systems. Specifically, we address inertia–based propelled vehicles, whose mass changes due to change of water amount in their water tanks and their configuration changes due to movable mass enabling maneuvers. They can be additionally subjected to constraints, which are imposed due to their desired performance, tracking pre-specified motions or are control constraints implying underactuation. The paper presents a new approach to variable mass and configuration systems modeling, which is based upon Boltzmann-Hamel equations derived in quasi-coordinates. The dynamics framework based upon quasi-coordinates results in dynamic models suitable for performance analysis and model-based control applications. It serves free or constrained variable mass and configuration systems modeling. The example researched in the paper is an underwater vehicle, which is purely inertia–based propelled. The theoretical development is illustrated with simulation studies of dynamical behavior and performance of the underwater vehicle model moving a specified trajectory.

Compliant net for AUV retrieval using a UAV

This paper addresses the problem of retrieving an autonomous underwater vehicle (AUV) with an unmanned aerial vehicle (UAV). The design of the compliant net that naturally adapts to the payload morphology is proposed as a retrieval tool. We constructed the proposed compliant net and conducted experiments with an underwater vehicle model as payload. Furthermore, mathematical model of a system comprised of the UAV and the payload is presented and a mid-ranging controller is utilized for such a system. We also present simulation results and analyze the stability of the UAV with the cable attached payload.

A Novel Extended State Observer for Output Tracking of MIMO Systems With Mismatched Uncertainty

In this paper, we develop a novel extended state observer (ESO), in terms of tracking error only, for output tracking of a class of multi-input multioutput systems with mismatched uncertainty. A novel ESO is constructed from the nonsmooth function “ $\text{fal}$ ” to estimate both uncertainty and state of the system. An ESO-based output feedback controller is then designed to compensate (cancel) the uncertainty and to achieve the output tracking. The convergence of the closed-loop system is proved. The effectiveness of the proposed method is demonstrated by numerical results of trajectory tracking for a practical autonomous underwater vehicle model. We show that in the presence of measurement noise, this novel ESO leads to better performance than the linear ESO. Moreover, this type of ESO has much smaller peaking value than the linear ESO under the same tuning gain.

Steering Control of an Underwater Vehicle using Adaptive Back Stepping Approach

This paper presents a simple and systematic approach to steer an underwater vehicle model by considering two different cases: (i) when all actuators are functional, and (ii) when one actuator is not working. In first case, the model of an underwater vehicle is steered by using adaptive Backstepping technique. The first actuator is necessary for the operation of the system so any of the other three actuators can be non-operational. So, the second case itself contains three different cases. Adaptive Backstepping is then used to steer the system with one non-working actuator. The synthesis method is general, in that it applies to a large class of drift free, completely controllable systems, for which the associated controllability Lie algebra is locally nilpotent.

Non-adaptive velocity tracking controller for a class of vehicles

AbstractA non-adaptive controller for a class of vehicles is proposed in this paper. The velocity tracking controller is expressed in terms of the transformed equations of motion in which the obtained inertia matrix is diagonal. The control algorithm takes into account the dynamics of the system, which is included into the velocity gain matrix, and it can be applied for fully actuated vehicles. The considered class of systems includes underwater vehicles, fully actuated hovercrafts, and indoor airship moving with low velocity (below 3 m/s) and under assumption that the external disturbances are weak. The stability of the system under the designed controller is demonstrated by means of a Lyapunov-based argument. Some advantages arising from the use of the controller as well as the robustness to parameters uncertainty are also considered. The performance of the proposed controller is validated via simulation on a 6 DOF robotic indoor airship as well as for underwater vehicle model.

Autonomous underwater vehicle precise motion control for target following with model uncertainty

Target following plays an important role in oceanic detection and target capturing for autonomous underwater vehicles. Due to the model nonlinearity and external disturbance, the dynamic model of a portable autonomous underwater vehicle was usually established with parameter uncertainties. In this article, a petri-based recurrent type 2 fuzzy neural network has been built to approximate the unknown autonomous underwater vehicle dynamics. The type 2 fuzzy logic system has been applied to the network to improve the approximation accuracy for systematic nonlinearity, and the petri layer in the network can improve estimation speed and reduce energy consumption. A petri-based recurrent type 2 fuzzy neural network–based adaptive robust controller has been proposed for target tracking. In the offshore experiments, the proposed controller has not only realized stable position and pose control but also successfully followed mobile target on the surface. In the tank underwater experiments, the pipeline target has been successfully followed to further verify the controller performance.

Robust input design for nonlinear dynamic modeling of AUV

Input design has a dominant role in developing the dynamic model of autonomous underwater vehicles (AUVs) through system identification. Optimal input design is the process of generating informative inputs that can be used to generate the good quality dynamic model of AUVs. In a problem with optimal input design, the desired input signal depends on the unknown system which is intended to be identified. In this paper, the input design approach which is robust to uncertainties in model parameters is used. The Bayesian robust design strategy is applied to design input signals for dynamic modeling of AUVs. The employed approach can design multiple inputs and apply constraints on an AUV system’s inputs and outputs. Particle swarm optimization (PSO) is employed to solve the constraint robust optimization problem. The presented algorithm is used for designing the input signals for an AUV, and the estimate obtained by robust input design is compared with that of the optimal input design. According to the results, proposed input design can satisfy both robustness of constraints and optimality.

Sliding Mode Based Predictive Controller of a Spheroidal Underwater Vehicle

This paper focuses on hydrodynamic modeling and control of spheroidal underwater vehicle. The vehicle considered in this paper is appendage free and unstable. Water jet propulsion system is used in this vehicle. The dynamics of the vehicle is highly unstable due to munk moment. The spheroidal shape underwater robot is used in nuclear reactor inspection, port security inspection, defence and ocean surveillance where external appendages are not required. A new and innovative control technique, Sliding mode based model predictive control is introduced in this paper. Sliding mode control technique is used to stabilize the vehicle and once the vehicle model is stabilized it is easy to apply Model Predictive Control (MPC). Model Predictive control technique is used to control the heading of spheroidal underwater vehicle. Simulation results show that the Sliding mode based predictive control performance is better than simple PD control and state feedback controller.

Robust generalized dynamic inversion based control of autonomous underwater vehicles

A novel two-loop structured robust generalized dynamic inversion–based control system is proposed for autonomous underwater vehicles. The outer (position) loop of the generalized dynamic inversion control system utilizes proportional-derivative control of the autonomous underwater vehicle’s inertial position errors from the desired inertial position trajectories, and it provides the reference yaw and pitch attitude angle commands to the inner loop. The inner (attitude) loop utilizes generalized dynamic inversion control of a prescribed asymptotically stable dynamics of the attitude angle errors from their reference values, and it provides the required control surface deflections such that the desired inertial position trajectories of the vehicle are tracked. The dynamic inversion singularity is avoided by augmenting a dynamic scaling factor within the Moore–Penrose generalized inverse in the particular part of the generalized dynamic inversion control law. The involved null control vector in the auxiliary part of the generalized dynamic inversion control law is constructed to be linear in the pitch and yaw angular velocities, and the proportionality gain matrix is designed to guarantee global closed-loop asymptotic stability of the vehicle’s angular velocity dynamics. An additional sliding mode control element is included in the particular part of the generalized dynamic inversion control system, and it works to robustify the closed-loop system against tracking performance deterioration due to generalized inversion scaling, such that semi-global practically stable attitude tracking is guaranteed. A detailed six degrees-of-freedom mathematical model of the Monterey Bay Aquarium Research Institute autonomous underwater vehicle is used to evaluate the control system design, and numerical simulations are conducted to demonstrate closed-loop system performance under various types of autonomous underwater vehicle maneuvers, under both nominal and perturbed autonomous underwater vehicle system’s mathematical model parameters.

Application of Pre-stressed Steel Wire-winding Frame Technology in Deep-sea Hydrostatic Pressure Testing Equipment

This paper introduces the first application of the technology of pre-stressed steel wire-winding frame in constructing hydrostatic pressure testing equipment for the environment simulation of deep-sea. In December 2015, a 200 MPa medium size ultrahigh pressure cylinder (0.8 m in diameter, 2.5 m in height) was tested experimentally, and the technical indicators met the requirements, which was operated by institute of deep-sea science and engineering, CAS and Sichuan aerospace industry Chuanxi machine Co., Ltd. Then it provided full ocean depth pressure testing support for scaled model of titanium alloy personnel hull, buoyancy material and unmanned underwater vehicle. The results of all the experiments show that the technology of pre-stressed steel wire-winding frame has apparent advantages in constructing large volume and ultrahigh pressure testing equipment compared with traditional technology.

Uncertainty and disturbance estimator based sliding mode control of an autonomous underwater vehicle

In this paper, a highly non-linear model of an autonomous underwater vehicle (AUV) with six degrees-of-freedom is linearized to yaw (horizontal) and pitch (vertical) planes under several working conditions. For controlling steering and diving planes, an uncertainty disturbance estimator based sliding mode control (UDE-SMC) scheme is proposed and designed separately as single-input single-output controllers for horizontal and vertical plane dynamics of an AUV system. The proposed UDE-SMC scheme is effective in compensating the uncertainties in the hydrodynamic parameters of the vehicle and rejecting unpredictable disturbances due to ocean currents. The UDE-SMC consists of an equivalent and estimated lumped uncertain terms to suppress the effect of external disturbances and parametric uncertainties acting on the vehicle dynamics. Numerical simulations were performed to validate the UDE-SMC.

Fully actuated model-based control with six-degree-of-freedom coupled dynamical plant models for underwater vehicles: Theory and experimental evaluation

This paper reports a comparative experimental evaluation of one non-model-based proportional derivative (PD) six-degree-of-freedom (6-DOF) controller and two model-based 6-DOF controllers designed to enable low-speed, neutrally buoyant, and fully actuated underwater vehicles to perform 6-DOF set-point regulation and trajectory tracking. We show analytically that the non-model-based PD controller provides locally asymptotically stable set-point regulation, and we show analytically that the model-based controllers provide locally asymptotically stable 6-DOF trajectory tracking. Numerical simulation studies are reported that corroborate the analytical stability results. We report the first comparative experimental evaluation of three different control algorithms for dynamic 6-DOF trajectory tracking of fully actuated underwater vehicles. Experimental results with the Johns Hopkins University remotely operated vehicle (JHU ROV) show that the model-based controllers’ mean absolute position and velocity tracking error is significantly smaller than the non-model-based PD controller for coupled maneuvers. The model-based controllers are shown to outperform the non-model-based controllers over a wide range of variations in the magnitude of derivative feedback gain. The velocity tracking error of the model-based controllers is shown to be on the same order of magnitude as the measurement error of the velocity sensing instrumentation.

Parametric identification and structure searching for underwater vehicle model using symbolic regression

Models for underwater vehicle explaining its relationship between movement and the force exerting on the robot permit a wide range of development to be used in control and navigation. Yet currently no general method arrives a better model with structure and parameters for vehicles automatically. Based on the empirical data, symbolic regression method inspired by natural selection is conducted to automatically detect realistic structure and parameters of vehicle model. The proposed method is completely general and does not assume any pre-existing models before identification, it can be applied “out of the box” to any given vehicle experiment data. To validate and compare our approach with parameter identification methods like Levenberg–Marquardt Algorithm and Genetic Algorithm, we systematically rediscover the laws underlying underwater vehicle models and neglected laws for reflect the environments. Predicted results for datasets show that we are able to find programs that are simple enough to lead to an actual accurate model for describing the mechanisms of the vehicle.

Actuator fault diagnosis of autonomous underwater vehicle based on improved Elman neural network

Autonomous underwater vehicles (AUV) work in a complex marine environment. Its system reliability and autonomous fault diagnosis are particularly important and can provide the basis for underwater vehicles to take corresponding security policy in a failure. Aiming at the characteristics of the underwater vehicle which has uncertain system and modeling difficulty, an improved Elman neural network is introduced which is applied to the underwater vehicle motion modeling. Through designing self-feedback connection with fixed gain in the unit connection as well as increasing the feedback of the output layer node, improved Elman network has faster convergence speed and generalization ability. This method for high-order nonlinear system has stronger identification ability. Firstly, the residual is calculated by comparing the output of the underwater vehicle model (estimation in the motion state) with the actual measured values. Secondly, characteristics of the residual are analyzed on the basis of fault judging criteria. Finally, actuator fault diagnosis of the autonomous underwater vehicle is carried out. The results of the simulation experiment show that the method is effective.

Computation of Empowerment for an Autonomous Underwater Vehicle

The paper addresses the computation of the information-theoretic empowerment measure for a simplified vertical plane dynamic model of an eFolaga autonomous underwater vehicle. Empowerment can be used to measure how much influence a vehicle has on its behavior, and it identifies desirable states of the vehicle, hence it can act as an intrinsic cost function to use during a mission. Online empowerment computation can be exploited within complex autonomous robotics missions. The proposed approach is being developed in the framework of a H2020 underwater robotics research project (Widely scalable Mobile Underwater Sonar Technology) to improve the autonomy of autonomous underwater vehicles for acoustic seismic applications.

A Dynamic Model for Underwater Vehicle Maneuvering Near a Free Surface

Abstract: This paper introduces a physics-based and control-oriented underwater vehicle model for near-surface operations. To construct the model, we follow an energy-based Lagrangian approach, where the presence of the free surface is incorporated using a free surface Lagrangian. This effectively modifies the system energy commonly used to derive the Kirchhoff equations, which govern underwater vehicle motion in an unbounded ideal fluid. The system Lagrangian is then used to derive the 6-DOF equations of motion for an underwater vehicle maneuvering near the free surface in otherwise calm seas. To illustrate the additional capabilities of the proposed model, we present an analytical hydrodynamic solution for a circular cylinder traveling parallel to the free surface. Comparisons are also drawn between the proposed model and the Cummins model (Cummins, 1962). While Cummins’ model exactly satisfies the free surface boundary condition and approximately satisfies the body boundary condition, we choose to exactly satisfy the body boundary condition and approximately satisfy the free surface condition. This exchange removes the restriction that limits the Cummins equations to slow-maneuvering in a seaway.