Underwater Vehicle Dynamics Research Articles

An underwater vehicles dynamics in the presence of noise and Fokker-Planck equations

In systems and control literature, underwater vehicles are studied extensively. The translational equations of motion coupled with rotational equations, which encompass the evolution of linear velocity vector in combination with angular velocity vector, describe the equation of motion of underwater vehicles. As a result of this, the underwater vehicle dynamics assumes the structure of a six-dimensional ordinary differential equation. Ocean current circulations influence stochastically the motion of underwater vehicles. In this paper, first we account the underwater vehicle-ocean current interaction in the stochastic framework that leads to a multi-dimensional Stochastic Differential Equation (SDE). Secondly, we accomplish the noise analysis of underwater vehicles using the Fokker-Planck technique. This paper will be of interest to mechanists, control theorists, naval scientists looking for research in autonomous systems under noise influence. Further research on stochastically perturbed underwater vehicles will refine existing results available in literature.

Research on Motion Control of AUV with Hybrid Actuators

A switch function is presented to smooth control instructions and avoid system vibration during switch operating underwater vehicles with hybrid actuators. And a Modified S-plane Controller (MSC) is proposed by analyzing underwater vehicles dynamics and taking static force and coupling effects between the longitude velocity and other dimensions into account. Besides the advantages of S controller such as simple structure, MSC can solve the motion control of underwater vehicle at high speed which is difficult to control with S-plane controller. The stability of MSC is analyzed with Lyapunov function. Finally, MSC is applied to the motion control of an autonomous underwater vehicle controlled by rudders and thrusters. The feasibility of MSC is demonstrated by the results of velocity control, yaw control and depth control tests.

Inverse simulation and analysis of underwater vehicle dynamics using feedback principles

Inverse simulation is a technique used in the modelling of dynamic systems that allows time histories of input variables to be found that generate required model output responses and provide inverse solutions in cases where analytical approaches to model inversion can present difficulties. This paper describes the application of inverse simulation to a nonlinear dynamic model of an unmanned underwater vehicle (UUV) and the determination of vehicle control inputs for specified manoeuvres. The approach to inverse simulation used in this application is based on the principles of feedback. Design issues relating to the UUV control surfaces and propeller thrust are highlighted through this procedure. The paper includes an outline of the nonlinear model of the UUV and typical sets of experimental conditions. Feedback loops are designed around the model for selected output variables and the inverse solutions are generated through simulation of this multi-input multi-output closed-loop system. It is shown that the feedback approach can provide inverse solutions for an appropriate choice of loop-gain factors and integration time step using a fixed-step integration algorithm. Inverse solutions generated in this way are shown to provide insight concerning issues of vehicle handling and manoeuvrability in a more direct fashion than is possible using conventional simulation methods.

Identification modeling of underwater vehicles' nonlinear dynamics based on support vector machines

An identification method based on support vector machines (SVM) is proposed for modeling nonlinear dynamics of underwater vehicles (UVs), and a typical torpedo-shaped autonomous underwater vehicle (AUV) is employed for the purpose of validation. To obtain the hydrodynamic derivatives of the vehicle and the dynamical models of the thruster and fins, a series of hydrodynamic experiments are conducted by using vertical planar motion mechanism (PMM) and circulating water channel (CWC). Maneuvering simulation is carried out by using the hydrodynamic model obtained from experiments, and SVM is applied to identify the damping terms together with Coriolis and centripetal terms by analyzing the simulation data. By using the identified nonlinear model and experiment-based hydrodynamic model respectively, maneuvering simulations and control applications are implemented. The results are compared to verify the proposed method, and the effectiveness and good generalization performance of SVM in modeling the nonlinear dynamics of UVs are demonstrated.

Dynamics and navigation of autonomous underwater vehicles for submarine gravity surveying

We investigated the effect of autonomous underwater vehicle (AUV) dynamics and navigation on underway submarine gravimetry. Our research was motivated by the need to obtain spatially dense marine gravity measurements close to the source of subkilometer-scale geologic features in the shallow oceanic crust. Such measurements have been previously obtained, for instance, with piloted submarines and towed sleds; however, the high cost and, in the case of on-bottom measurements, poor spatial sampling preclude routine acquisition of these measurements. Continuous underway gravity surveys with AUVs is a compelling cost-effective option, but this method requires separating the AUV accelerations from the measured gravity. We show that AUVs with a large distance between the center of buoyancy and the center of gravity have lower vertical accelerations than torpedo-shaped AUVs and consequentially are better suited for underway gravity surveys. Furthermore state estimators, which combine sensor measurements and models of the vehicle’s motion, provide superior estimates of the vehicle’s vertical accelerations than methods used in previous underway submarine gravity surveys. We simulated the use of these navigation methods in detecting dike swarms at the East Pacific Rise. Analysis showed that we can shorten filters used in reducing gravity data and consequentially provide improved measurements of the free-water anomaly with a minimal detectable spatial wavelength approximately 65% lower than previously reported results.

Tracking Controller Design for Diving Behavior of an Unmanned Underwater Vehicle

The study has investigated the almost disturbance decoupling problem of nonlinear uncertain control systems via the fuzzy feedback linearization approach. The significant dedication of this paper is to organize a control algorithm such that the closed-loop system is active for given initial condition and bounded tracking trajectory with the input-to-state stability and almost disturbance decoupling performance. This study presents a feedback linearization controller for diving control of an unmanned underwater vehicle. Unmanned underwater vehicle proposes difficult control subject due to its nonlinear dynamics, uncertain models, and the existence of disturbances that are difficult to measure. In general, while investigating the diving dynamics of an unmanned underwater vehicle, the pitch angle is always assumed to be small. This assumption is a strong restricting constraint in many interesting practical applications and will be relaxed in this study.

Dynamics and robust control of underwater vehicles for depth trajectory following

This paper addresses the robust control synthesis of diving/climbing manoeuvres for underwater vehicle in the vertical plane. First, a new state–space representation for the vehicle dynamics is presented, and the corresponding problem formulation is clearly stated. Next, the two-controller set-up using a H∞-loop shaping design is employed to deal with the bottom following capability and robustness issues. Then the reduced order control system with a Hankel norm is evaluated in the frequency domain. In addition, the preview control approach is used to improve the overall tracking performance for undersea manoeuvres. The specific control tasks include the tracking of a set of depth profiles or ocean floors. Simulation results show that control objectives are effectively accomplished in spite of model uncertainties. Finally, it is found that the proposed control methodology is suitable for the depth trajectory following applications over a wide range of operating conditions.

Robust Autopilot Design for Unmanned Underwater Vehicle Based on H∞ Mixed Sensitivity

As the complexity of marine environment and the existence of uncertain factors along with Unmanned Underwater Vehicle (UUV) dynamics itself bringing many nonlinear problems, it is difficult to control the navigation in accordance with a predetermined trajectory. Based on the UUV dynamics analysis and system modeling, this paper decoupled its control system into three sub-control systems, designed robust autopilot using H∞ mixed sensitivity control algorithm to generate the low-order controller to achieve independent control of three degrees of freedom. Eventually, the underwater navigation simulation results show that the proposed method can provide better control performance and the method is feasible and high efficiency in actual applications.

Identification of a Variable Mass Underwater Vehicle Via Volterra Neural Network

The first step in designing a control system for a rigid body is to understand its dynamics. Underwater vehicle dynamics may be complex and difficult to model, mainly due to difficulties in observing and measuring actual underwater vehicle hydrodynamics response. This paper is concerned with structure selection of nonlinear polynomials in a Volterra polynomial basis function neural network and recursive parameter estimation of the selected model, in order to obtain a model of a variable mass underwater vehicle with six degrees of freedom using an input-output data set. The simulation results reveal the efficiency of the approach.

추진기의 동역학을 고려한 무인잠수정의 슬라이딩 모드 제어

무인잠수정의 동역학은 추진체의 동력학에 의해 큰 영향을 받는다. 무인잠수정의 호버링 또는 저속 상태의 움직임을 제어하는 것은 자동 도킹 혹은 잠수정의 매니퓰레이터의 제어에 있어서 매우 중요하다. 모터기반의 추진체 동역학은 비선형적이며 불확실한 매개변수를 가지고 있다. 결국, 추진기와 동적 커플링을 이루는 무인잠수정의 운동역학도 매우 비선형적이며 불확실한 매개변수를 가지고 있기 때문에 강인제어기가 무인잠수정의 모션제어에 있어서 효과적이라고 할 수 있다. 따라서 본 논문에서는 전기 추진체에 의해 추진되는 무인잠수정의 저속 또는 호버링 상태를 제어하기 위한 강인제어 기법을 보인다. 또한, 비선형성과 불확실한 매개변수가 결합된 무인잠수정의 상태도 강인제어를 이용하여 동시에 제어한다. 강인제어 방법 중에서 슬라이딩 모드 제어기를 설계하여 추진체와 무인잠수정의 불확실한 변수와 비선형성들을 보상하며 원하는 위치를 유지하는 제어방법을 제안하였다. 모의실험을 통하여 제안한 슬라이딩 모드 제어기는 선형제어기인 PD제어기 보다 성능이 우수함을 확인할 수 있었다.

Optimal Control of an Underwater Sensor Network for Cooperative Target Tracking

Optimal control (OC) is a general and effective approach for trajectory optimization in dynamical systems. So far, however, it has not been applied to mobile sensor networks due to the lack of suitable objective functions and system models. In this paper, an integral objective function representing the quality of service of a sensor network performing cooperative track detection over time is derived using a geometric transversals approach. A set of differential equations modeling the sensor network's dynamics is obtained by considering three dependent subsystems, i.e., underwater vehicles, onboard sensors, and oceanographic fields. Each sensor-equipped vehicle is modeled as a bounded subset of a Euclidian space, representing the sensor's field of view (FOV), which moves according to underwater vehicle dynamics. By this approach, the problem of generating optimal sensors' trajectories is formulated as an OC problem in computational geometry. The numerical results show that OC significantly improves the network's quality of service compared to area-coverage and path-planning methods. Also, it can be used to incorporate sensing and energy constraints on the sensors' state and control vectors, and to generate fronts of Pareto optimal trajectories.

Stable nonlinear adaptive controller for an autonomous underwater vehicle using neural networks

In general, the dynamics of autonomous underwater vehicles (AUVs) are highly nonlinear and their hydrodynamic coefficients vary with different operating conditions. For this reason, high performance control system for an AUV usually should have the capacities of learning and adaptation to the time-varying dynamics of the vehicle. In this article, we present a robust adaptive nonlinear control scheme for an AUV, where a linearly parameterized neural network (LPNN) is introduced to approximate the uncertainties of the vehicle's dynamics, and the basis function vector of the network is constructed according to the vehicle's physical properties. The proposed control scheme can guarantee that all of the signals in the closed-loop system are uniformly ultimately bounded (UUB). Numerical simulation studies are performed to illustrate the effectiveness of the proposed control scheme.

Symmetry and Reduction for Coordinated Rigid Bodies

Motivated by interest in the collective behavior of autonomous agents, we lay foundations for a study of networks of rigid bodies and, specifically, the problem of aligning orientation and controlling relative position across the group. Our main result is the reduction of the (networked) system for the case of two individuals coupled by control inputs that depend only on relative configuration. We use reduction theory based on semi-direct products, yielding Poisson spaces that enable efficient formulation of control laws. We apply these reduction results to satellite and underwater vehicle dynamics, proving stability of coordinated behaviors such as two underwater vehicles moving at constant speed with their orientations stably aligned.

QUASI-RANDOM, MANOEUVRE-BASED MOTION PLANNING ALGORITHM FOR AUTONOMOUS UNDERWATER VEHICLES

This paper presents an approach using a hybrid modelling technique known as Manoeuvre Automaton (MA) to capture the key dynamics of a nonlinear autonomous underwater vehicle (AUV) in such a way that high-level tasks such as optimal motion planning can be computationally simplified, while still allowing it to perform complicated manoeuvres when the situation arises. With respect to motion planning in an obstacle filled environment, an incremental stochastic technique derived from the Rapid-exploring Random Tree (RRT) algorithm is applied. This paper proposes a multiple nested node version of RRT and also addresses the case of a time varying final state. Simulation results as presented, using a 3 degree-of-freedom (DOF) nonlinear AUV model in order to prove the viability of the concept.

Identification of underwater vehicles by a Lyapunov method

This paper deals with the identification of non linear multivariable models of underwater vehicles by using a novel Lyapunov-based method. The method operates in the continuous time domain and can be applied to non linear models that are linear with respect to an unknown parameter vector. After an introduction, where the role of identification methods in the area of guidance and control of underwater vehicles is outlined, some mathematical models that are generally used for describing the dynamics of underwater vehicles are concisely presented. The main features of the identification method are then illustrated through a simulation example concerning the surge dynamics of an underwater vehicle.

Reduction of Controlled Lagrangian and Hamiltonian Systems with Symmetry

We develop reduction theory for controlled Lagrangian and controlled Hamiltonian systems with symmetry. Reduction theory for these systems is needed in a variety of examples, such as a spacecraft with rotors, a heavy top with rotors, and underwater vehicle dynamics. One of our main results shows the equivalence of the method of reduced controlled Lagrangian systems and that of reduced controlled Hamiltonian systems in the case of simple mechanical systems with symmetry.

신경회로망을 이용한 자율무인잠수정의 적응제어

Since the dynamics of autonomous underwater vehicles (AUVs) are highly nonlinear and their hydrodynamic coefficients vary with different vehicle's operating conditions, high performance control systems of AUVs are needed to have the capacities of teaming and adapting to the variations of the vehicle's dynamics. In this paper, a linearly parameterized neural network (LPNN) is used to approximate the uncertainties of the vehicle dynamics, where the basis function vector of the network is constructed according to the vehicle's physical properties. The network's reconstruction errors and the disturbances in the vehicle dynamics are assumed be bounded although the bound may be unknown. To attenuate this unknown bounded uncertainty, a certain estimation scheme for this unknown bound is introduced combined with a sliding mode scheme. The proposed controller is proven to guarantee that all signals in the closed-loop system are uniformly ultimately bounded (UUB). Numerical simulation studies are performed to illustrate the effectiveness of the proposed control scheme.

An on-line intelligent multi-input—multi-output autopilot design study

This paper describes the development of an on-line intelligent multi-input-multi-output autopilot for simultaneously controlling the yaw and roll dynamics of an autonomous underwater vehicle (AUV) model. The six-degree-of-freedom non-linear AUV model used in the design study is hosted in the MATLAB/Simulink environment. Simulation results are presented that demonstrate the ability of the autopilot to operate effectively in the presence of mass variation in the vehicle model and measurement noise. Thus, the approach adopted offers an attractive alternative to more traditional methodologies.

Robust control for a class of modified Duffing equations

In this paper, a new class of modified Duffing equations with coupled periodic forcing and damping terms is developed, and the robust control problem for the system with structural uncertainty is studied systematically. This type of modified Duffing equations originates from swimming dynamics of a shape-changing underwater vehicle. The set-point control problem is studied for the system with constant disturbance and general structural uncertainty separately. Then, the trajectory tracking problem is investigated based on the discontinuous Lyapunov control and the sliding mode control. These presented approaches do not require any statistical information of the uncertainties; only their bounds are used in the design. Simulation results are given to demonstrate the effectiveness of the control algorithms.

A Fuzzy Autopilot Design Approach that Utilizes Non-Linear Consequent Terms

This paper describes the development of a fuzzy autopilot for controlling the non-linear yaw dynamics of an autonomous underwater vehicle (AUV) model. The autopilot design is based on a new approach that uses a Gaussian fuzzy inference mechanism. For the design study, the AUV model is hosted in the MATLAB/Simulink environment. Simulation results are presented which illustrate the potential of this novel methodology.